diff --git a/README.md b/README.md
index e290bd628df213754b268d8aa31c7b9bf1e3fbb6..3e606ce989899b8779158954e6661e99a37f213b 100644
--- a/README.md
+++ b/README.md
@@ -9,11 +9,10 @@ We will use GitLab's issue tracker as a discussion forum. Feel free to [open an
# First-week tasks
-1. Join [the Discord](https://discord.gg/hJsreTeZjc)
+1. Join [the Discord](https://discord.gg/tgbPcCFSm2)
1. Log into gitlab: https://gitlab.epfl.ch/users/sign_in
-1. Please fill in [this table](https://docs.google.com/spreadsheets/d/1rcq_UMgR6bAH-iK1L2I6WoClZtCqUoIOLqQO3NJKdgg) with your GASPAR and SCIPER number
- * Choose the group for the exercises
- * This will initialize your GitLab repository for the course
+1. Please fill in [this sheet](https://forms.gle/N6F3Q3jZm71AASby9) with your GASPAR and SCIPER number
+1. PLease fill in [this doodle](https://doodle.com/poll/yi2c33zkb8nre3ug) by picking the room in which you will do the exercises.
1. Follow the [Tools Setup](labs/tools-setup.md) page.
1. Do the [example lab](labs/example-lab.md).
1. Watch all videos under *Parallelism 1: Introduction to Parallel Programming* below
@@ -27,41 +26,28 @@ The grading of the course is divided between exercies (5%), labs (25%), midterm
| Role | People |
| :--- | :--- |
-| Professors | [Martin Odersky](https://people.epfl.ch/martin.odersky), [Viktor Kunčak](https://people.epfl.ch/viktor.kuncak) |
-| TAs | [Aleksander Boruch-Gruszecki](https://people.epfl.ch/aleksander.boruch-gruszecki), [Dragana Milovancevic](https://people.epfl.ch/dragana.milovancevic), [Guillaume Martres](https://people.epfl.ch/guillaume.martres), [Nicolas Stucki](https://people.epfl.ch/nicolas.stucki), [Olivier Blanvillain](https://people.epfl.ch/olivier.blanvillain) |
-| Student TAs | [Antoine Masanet](https://people.epfl.ch/antoine.masanet), [Lucas Giordano](https://people.epfl.ch/lucas.giordano), [Kajetan Pyszkowski](https://people.epfl.ch/kajetan.pyszkowski), [Marco Vögeli](https://people.epfl.ch/marco.vogeli), [Quentin Guignard](https://people.epfl.ch/quentin.guignard), [Sara Djambazovska](https://people.epfl.ch/sara.djambazovska) |
+| Professors | [Martin Odersky](https://people.epfl.ch/martin.odersky), [Kashyap Sanidhya](https://people.epfl.ch/sanidhya.kashyap) |
+| TAs | [Dragana Milovancevic](https://people.epfl.ch/dragana.milovancevic), [Guillaume Martres](https://people.epfl.ch/guillaume.martres), [Matthieu Bovel](https://people.epfl.ch/matthieu.bovel), [Simon Guilloud](https://people.epfl.ch/simon.guilloud), [Tao Lyu](https://people.epfl.ch/tao.lyu), [Gupta Vishal](https://people.epfl.ch/vishal.gupta)|
+| Student TAs | Mohamed Boukhari, Nicolas Matekalo, Tuomas Pääkkönen, Abel Wilkinson |
# Course Schedule
-Lectures are partially live (on Zoom) and partially prerecorded (on YouTube).
-Live sessions will be held on Wednesdays from 14:15 to 16:00.
-Weekly Discord sessions will be held on Wednesdays from 14:15 to 16:00 for exercises (if it is a week with exercises) and 16:15 to 18:00 for labs.
+Lectures are partially live and partially prerecorded (on YouTube).
+Live sessions will be held on Wednesdays from 14:15 to 16:00 if it is a week with live lecture.
+Exercise sessions will be held on Wednesdays from 14:15 to 16:00 if it is a week with exercises.
+Lab sessions will be held on Wednesdays from 16:15 to 18:00.
You should watch the prerecorded lectures before doing the exercies.
-In the first week of the semester, there will be a live Zoom session on Wednesday at 14:15 to welcome you to the class and answer questions you might have, followed by a Discord session.
<!-- seq 0 7 100 | xargs -i date -d "02/24/2021 {} days" +"%d.%m.%Y" -->
-| Week | Date | Topic | Lectures (14:15-16:00) | Exercises (14:15-16:00) | Labs (16:15-18:00) |
-| :-- | :-- | :-- | :-- | :-- | :-- |
-| 1 | 24.02.21 | Parallelism 1 | Prerecorded | Welcome Zoom session | Lab 1 |
-| 2 | 03.03.21 | Parallelism 2 | Prerecorded | Exercise 1 | Lab 1 & 2 |
-| 3 | 10.03.21 | Parallelism 3 | Prerecorded | Exercise 2 | Lab 2 & 3 |
-| 4 | 17.03.21 | Parallelism 4 | Prerecorded | Exercise 3 | Lab 3 & 4 |
-| 5 | 24.03.21 | Concurrency 1 | Live | | Lab 4 & 5 |
-| 6 | 31.03.21 | Concurrency 2 | Live | | Lab 5 & 6 |
-| 7 | 07.04.21 | _Easter_ | | | |
-| 8 | 14.04.21 | **Midterm Exam** | | | |
-| 9 | 21.04.21 | Concurrency 3 | Live | | Lab 6 |
-| 10 | 28.04.21 | Actors 1 | Prerecorded | Exercise 4 | Lab 7 |
-| 11 | 05.05.21 | Actors 2 | Prerecorded | Exercise 5 | Lab 7 |
-| 12 | 12.05.21 | Spark 1 | Prerecorded | Exercise 6 | Lab 8 |
-| 13 | 19.05.21 | Spark 2 | Prerecorded | Exercise 7 | Lab 8 & 9 |
-| 14 | 26.05.21 | Spark 3 | Prerecorded | Exercise 8 | Lab 9 |
-| 15 | 02.06.21 | **Review for Final Exam** | | | |
+| Week | Date | Topic | Lectures (14:15-16:00) | Exercises (14:15-16:00) | Labs (16:15-18:00) |
+| :-- | :-- | :-- | :-- | :-- | :-- |
+| 1 | 2022.02.23 | Parallelism 1 | Prerecorded | Welcome Zoom session | Lab 1 |
+
Solutions to the exercises are released after each deadline. We do not provide solutions for the labs.
-Before each Discord session, students should watch videos corresponding to that week's topic:
+Before each Exercise session, students should watch videos corresponding to that week's topic:
### Intro
- [Welcome Zoom session][Zoom1]
@@ -102,97 +88,29 @@ Before each Discord session, students should watch videos corresponding to that
- [Amortized, Constant-Time Append Operation](https://www.youtube.com/watch?v=Ic5DUZLITVI)
- [Conc-Tree Combiners](https://www.youtube.com/watch?v=aLfFlCC1vjc)
-### Concurrency 1, 2 & 3
-
-- Live lectures
- - Concurrency 1 ([Zoom Recording][ZoomConcurrency1]) ([YouTube version][YouTubeConcurrency1])
- - Concurrency 2 ([Zoom Recording][ZoomConcurrency2]) ([YouTube version][YouTubeConcurrency2])
- - Concurrency 3 ([Zoom Recording][ZoomConcurrency3]) ([YouTube version][YouTubeConcurrency3])
-
-### Actors 1
-
-- [Introduction: why actors?](https://www.youtube.com/watch?v=ZQAe9AItH8o)
-- [The Actor Model](https://www.youtube.com/watch?v=c49tDZuFtPA)
-
-### Actors 2
-
-- [Message Processing Semantics](https://www.youtube.com/watch?v=Uxn1eg6R0Fc)
-- [Designing Actor Systems](https://www.youtube.com/watch?v=uxeMJLo3h9k)
-- [Testing Actor Systems](https://www.youtube.com/watch?v=T_2nwLr-H2s)
-
-### Spark 1: Spark Basics
-
-- [From Parallel to Distributed](https://www.youtube.com/watch?v=bfMbJ8NzTZI)
-- [Latency](https://www.youtube.com/watch?v=igNIz2Ent5E)
-- [RDDs, Spark's Distributed Collection](https://www.youtube.com/watch?v=EuVmW62aIXI)
-- [RDDs: Transformations and Actions](https://www.youtube.com/watch?v=qJlfATheS38)
-- [Evaluation in Spark: Unlike Scala Collections!](https://www.youtube.com/watch?v=0pVYuuUrN74)
-- [Cluster Topology Matters](https://www.youtube.com/watch?v=lS4vRzwrmtU)
-
-### Spark 2: Reduction Operations & Distributed Key-Value Pairs
-
-- [Reduction Operations](https://www.youtube.com/watch?v=JhF0_Ka_iqU)
-- [Pair RDDs](https://www.youtube.com/watch?v=kIUzgweDMUs)
-- [Transformations and Actions on Pair RDDs](https://www.youtube.com/watch?v=ovf0GFbnp5g)
-- [Joins](https://www.youtube.com/watch?v=kYpaZpj4qTM)
-
-### Spark 3: Partitioning and Shuffling
-
-- [Shuffling: What it is and Why it's important](https://www.youtube.com/watch?v=LrgA4PrKrks)
-- [Partitioning](https://www.youtube.com/watch?v=sTcki6mxjcA)
-- [Optimizing with Partitioners](https://www.youtube.com/watch?v=4Vfp5kp2jnE)
-- [Wide vs Narrow Dependencies](https://www.youtube.com/watch?v=L9BnaYp10c8)
-
# Labs
Labs are individual assignments where you get to write Scala programs using the concepts learned during lectures.
Labs are submitted by pushing your code on GitLab, see details in the [grading and submission](labs/grading-and-submission.md) page.
-| Labs | Name | Start date | Due date (23:59 [AoE](https://en.wikipedia.org/wiki/Anywhere_on_Earth)) |
-| :-- | :-- | :-- | :-- |
-| Lab 1 | Parallel Box Blur Filter | 24.02.21 | 07.03.2021 |
-| Lab 2 | Reductions and Prefix Sums | 01.03.21 | 14.03.2021 |
-| Lab 3 | K-Means | 08.03.21 | 21.03.2021 |
-| Lab 4 | Barnes-Hut Simulation | 15.03.21 | 28.03.2021 |
-| Lab 5 | Bounded Buffer | 22.03.21 | 04.04.2021 |
-| Lab 6 | Lock-free Sorted List | 29.03.21 | 25.04.2021 |
-| Lab 7 | Actors Binary Tree | 26.04.21 | 09.05.2021 |
-| Lab 8 | Wikipedia | 10.05.21 | 23.05.2021 |
-| Lab 9 | StackOverflow | 17.05.21 | 30.05.2021 |
+| Labs | Name | Start date | Due date (23:59 [AoE](https://en.wikipedia.org/wiki/Anywhere_on_Earth)) |
+| :-- | :-- | :-- | :-- |
+| Lab 1 | Parallel Box Blur Filter | 2022.02.23 | 2022.03.06 |
# Exercises
Exercises are pen and paper style questions that will help you consolidate the knowledge learned during lectures.
-Exercises should be done in groups and submitted on GitLab.
-You should form groups of up to five students for each exercise, solve the exercise remotely with your group (using Discord, Hangouts, Zoom, ...), and write your solutions in a text file.
-The first line of your solution file should list all the group members' SCIPER numbers.
-After you solve the exercise with your group, **each member should submit a copy of this file** to their GitLab repository following the instructions given in the problem statement.
-Exercises will be given a participation grade at the end of the semester, which accounts for 5% of the overall course grade.
-
-| Exercises | Start date | Due date (23:59 [AoE](https://en.wikipedia.org/wiki/Anywhere_on_Earth)) |
-| :-- | :-- | :-- |
-| Exercise 1 | 01.03.2021 | 07.03.2021 |
-| Exercise 2 | 08.03.2021 | 14.03.2021 |
-| Exercise 3 | 15.03.2021 | 21.03.2021 |
-| Exercise 4 | 26.04.2021 | 02.05.2021 |
-| Exercise 5 | 03.05.2021 | 09.05.2021 |
-| Exercise 6 | 10.05.2021 | 16.05.2021 |
-| Exercise 7 | 17.05.2021 | 23.05.2021 |
-| Exercise 8 | 24.05.2021 | 30.05.2021 |
+Exercises should be done in groups during the lecture.
# Exams
-The midterm exam will take place on 14.04.21. The midterm exam will cover all the material seen in the class up to week 6 (included).
+The midterm exam will take place on 2022.??.??. The midterm exam will cover all the material seen in the class up to week 6 (included).
-~~The final exam will take place on 02.06.21.~~ The final exam for our course, parallelism and concurrency has been scheduled for Saturday July 10th from 8.15 to 11.15 in rooms SG1, AAC137, CO1 and CE6. The final exam will cover all material seen during the semester.
+The final exam will take place on 2022.??.??. The final exam will cover all material seen during the semester.
Information about exams organization will be communicated by email.
-[Zoom1]: https://epfl.zoom.us/rec/share/wKUHvD1vNyczKpb2e161QCnqi3BtURL5D9zfVie3iXqXmrsIbJ2lrl4cCO_1FgSx.ZfvYlrRfSAFFdMcq?startTime=1614170578000
-[ZoomConcurrency1]: https://epfl.zoom.us/rec/play/LzV8eccIFbEgHMi-DLJqynUus7chng8NYyR7XeA5Jjn_NGqkeAhIiTNsOLmIOp0XJHfjrNeB-XS6F5Fw.47WW7-mux-mqQZN8?startTime=1616586685000
-[ZoomConcurrency2]: https://epfl.zoom.us/rec/play/nIeJO-yRcbqmjzIZWU4yU3gQC5h8rQlebVxbWSUEP426NvwgSsmis9lOOyV-m7SjVqFV9ML0vggMDg5N.01wR77_gQbSIUv8b?autoplay=true
-[ZoomConcurrency3]: https://epfl.zoom.us/rec/share/Y-CKNw08e88aeAhRhApCePes-1PWj97C6x6imLRM_X4kepGNjMjZimwDXCd4wGuk.hEUUleJEvt8MClDx?startTime=1619005278000
[YouTubeConcurrency1]: https://www.youtube.com/watch?v=5oUpSoUoII4
[YouTubeConcurrency2]: https://www.youtube.com/watch?v=Jvo-vrxaGnk
[YouTubeConcurrency3]: https://www.youtube.com/watch?v=t4tqMzfvclk
diff --git a/exercises/exercise-2.md b/exercises/exercise-2.md
deleted file mode 100644
index a08dfc9774fb03fe06c4e1c05a35d6864f97167f..0000000000000000000000000000000000000000
--- a/exercises/exercise-2.md
+++ /dev/null
@@ -1,121 +0,0 @@
-# Exercise 2
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b exercise-2 git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git exercise-2
-```
-
-Update the README.md file with your solutions. Don't forget to list the group members's SCIPER numbers.
-
-# Problem 1: Aggregate
-
-In this week's lecture, you have been introduced to the aggregate method of `ParSeq[A]` (and other parallel data structures...). It has the following signature:
-
-```scala
-def aggregate[B](z: B)(f: (B, A) => B, g: (B, B) => B): B
-```
-
-Discuss, as a group, what aggregate does and what its arguments represent.
-
-## Question 1
-
-Consider the parallel sequence `xs` containing the three elements `x1`, `x2` and `x3`. Also consider the following call to aggregate:
-
-```scala
-xs.aggregate(z)(f, g)
-```
-
-The above call might potentially result in the following computation:
-
-```scala
-f(f(f(z, x1), x2), x3)
-```
-
-But it might also result in other computations. Come up with at least 2 other computations that may result from the above call to `aggregate`.
-
-## Question 2
-
-Below are other examples of calls to aggregate. In each case, check if the call can lead to different results depending on the strategy used by `aggregate` to aggregate all values contained in `data` down to a single value. You should assume that `data` is a parallel sequence of values of type `BigInt`.
-
-Variant 1
-
-```scala
-data.aggregate(1)(_ + _, _ + _)
-```
-
-Variant 2
-
-```scala
-data.aggregate(0)((acc, x) => x - acc, _ + _)
-```
-
-Variant 3
-
-```scala
-data.aggregate(0)((acc, x) => acc - x, _ + _)
-```
-
-Variant 4
-
-```scala
-data.aggregate(1)((acc, x) => x * x * acc, _ * _)
-```
-
-## Question 3
-
-Under which condition(s) on `z`, `f`, and `g` does aggregate always lead to the same result?
-Come up with a formula on `z`, `f`, and `g` that implies the correctness of aggregate.
-
-*Hint*: You may find it useful to use calls to `foldLeft(z)(f)` in your formula(s).
-
-## Question 4
-
-Implement `aggregate` using the methods `map` and/or `reduce` of the collection you are defining aggregate for.
-
-## Question 5
-
-Implement `aggregate` using the `task` and/or `parallel` constructs seen in the first week and the `Splitter[A]` interface seen in this week's lecture. The `Splitter` interface is defined as:
-
-```scala
-trait Splitter[A] extends Iterator[A] {
- def split: Seq[Splitter[A]]
- def remaining: Int
-}
-```
-
-You can assume that the data structure you are defining aggregate for already implements a `splitter` method which returns an object of type `Splitter[A]`.
-
-Your implementation of `aggregate` should work in parallel when the number of remaining elements is above the constant THRESHOLD and sequentially below it.
-
-*Hint*: `Iterator`, and thus `Splitter`, implements the `foldLeft` method.
-
-## Question 6
-
-Discuss the implementations from questions 4 and 5. Which one do you think would be more efficient?
-
-# Problem 2: Depth
-
-Review the notion of depth seen in the lecture. What does it represent?
-
-Below is a formula for the depth of a *divide and conquer* algorithm working on an array segment of *size L*, as a function of *L*. The values *c*, *d* and *T* are constants. We assume that *L>0* and *T>0*.
-
-
-
-Below the threshold *T*, the algorithm proceeds sequentially and takes time *c* to process each single element. Above the threshold, the algorithm is applied recursively over the two halves of the array. The results are then merged using an operation that takes *d* units of time.
-
-## Question 1
-
-Is it the case that for all *1 ≤ L1 ≤ L2* we have *D(L1) ≤ D(L2)*?
-
-If it is the case, prove the property by induction on *L*. If it is not the case, give a counterexample showing values of *L1*, *L2*, *T*, *c*, and *d* for which the property does not hold.
-
-## Question 2
-
-Prove a logarithmic upper bound on *D(L)*. That is, prove that *D(L)* is in *O(log(L))* by finding specific constants *a*, *b* such that *D(L) ≤ a * log2(L) + b*.
-
-*Hint:* The proof is more complex that it might seem. One way to make it more manageable is to define and use a function *D'(L)* that has the property described in question 1, and is greater or equal to *D(L)*. We suggest you use:
-
-
-
-Also remark that computing *D'(L)* when *L* is a power of 2 is easy. Also remember that there always exists a power of 2 between any positive integer and its double.
diff --git a/exercises/exercise-3.md b/exercises/exercise-3.md
deleted file mode 100644
index 07f3d50791e2f8f84f388f12f9a496db46567f69..0000000000000000000000000000000000000000
--- a/exercises/exercise-3.md
+++ /dev/null
@@ -1,137 +0,0 @@
-# Exercise 3
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b exercise-3 git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git exercise-3
-```
-
-Update the README.md file with your solutions. Don't forget to list the group members's SCIPER numbers.
-
-# Problem 1: Parallel Encoding
-
-In this exercise, your group will devise a parallel algorithm to encode sequences using the run-length encoding scheme. The encoding is very simple. It transforms sequences of letters such that all subsequences of the same letter are replaced by the letter and the sequence length. For instance:
-
-```
-"AAAAATTTGGGGTCCCAAC" ⇒ "A5T3G4T1C3A2C1"
-```
-
-Your goal in this exercise is to come up with a parallel implementation of this algorithm. The function should have the following shape:
-
-```scala
-def rle(data: ParSeq[Char]): Buffer[(Char, Int)] =
- data.aggregate(???)(???, ???)
-```
-
-The Buffer class is already given to you. A buffer of type `Buffer[A]` represents sequences of elements of type `A`. It supports the following methods, all of which are efficient:
-
-```scala
-def isEmpty: Boolean // Checks if the buffer is empty.
-def head: A // Returns the first element of the buffer.
-def tail: Buffer[A] // Returns the buffer minus its first element.
-def last: A // Returns the last element of the buffer.
-def init: Buffer[A] // Returns the buffer minus its last element.
-def ++(that: Buffer[A]): Buffer[A] // Concatenate two buffers.
-def append(elem: A): Buffer[A] // Appends a single element to the right.
-
-Buffer.empty[A]: Buffer[A] // Returns an empty buffer.
-Buffer.singleton[A](element: A): Buffer[A] // Single element buffer.
-```
-
-# Problem 2: Parallel Two Phase Construction
-
-In this exercise, you will implement an array Combiner using internally a double linked list (DLL). Below is a minimal implementation of the `DLLCombiner` class and the related `Node` class. Your goal for this exercise is to complete the implementation of the (simplified) Combiner interface of the `DLLCombiner` class.
-
-```scala
-class DLLCombiner[A] extends Combiner[A, Array[A]] {
- var head: Node[A] = null // null for empty lists.
- var last: Node[A] = null // null for empty lists.
- var size: Int = 0
-
- // Implement these three methods...
- override def +=(elem: A): Unit = ???
- override def combine(that: DLLCombiner[A]): DLLCombiner[A] = ???
- override def result(): Array[A] = ???
-}
-
-class Node[A](val value: A) {
- var next: Node[A] // null for last node.
- var previous: Node[A] // null for first node.
-}
-```
-
-**Question 1:** What computational complexity do your methods have? Are the actual complexities of your methods acceptable according to the `Combiner` requirements?
-
-**Question 2:** One of the three methods you have implemented, `result`, should work in parallel according to the `Combiner` contract. Can you think of a way to implement this method efficiently using 2 parallel tasks?
-
-**Question 3:** Can you, given the current internal representation of your combiner, implement `result` so that it executes efficiently using 4 parallel tasks? If not, can you think of a way to make it possible?
-
-*Hint:* This is an open-ended question, there might be multiple solutions. In your solution, you may want to add extra information to the class Node and/or the class DLLCombiner.
-
-# Problem 3: Pipelines
-
-In this exercise, we look at pipelines of functions. A pipeline is simply a function which applies its argument successively to each function of a sequence. To illustrate this, consider the following pipeline of 4 functions:
-
-```scala
-val p: Int => Int = toPipeline(ParSeq(_ + 1, _ * 2, _ + 3, _ / 4))
-```
-
-The pipeline `p` is itself a function. Given a value `x`, the pipeline `p` will perform the following computations to process it. In the above example,
-
-```scala
-p(x) = (((x + 1) Application of first function
- * 2) Application of second function
- + 3) Application of third function
- / 4 Application of fourth function
-```
-
-In this exercise, we will investigate the possibility to process such pipelines in parallel.
-
-**Question 1:** Implement the following `toPipeline` function, which turns a parallel sequence of functions into a pipeline. You may use any of the parallel combinators available on `ParSeq`, such as the parallel `fold` or the parallel `reduce` methods.
-
-```scala
-def toPipeline(fs: ParSeq[A => A]): A => A = ???
-```
-
-*Hint:* Functions have a method called andThen, which implements function composition: it takes as argument another function and also returns a function. The returned function first applies the first function, and then applies the function passed as argument to that result. You may find it useful in your implementation of pipeline.
-
-**Question 2:** Given that your `toPipeline` function works in parallel, would the pipelines it returns also work in parallel? Would you expect pipelines returned by a sequential implementation of toPipeline to execute any slower? If so, why?
-
-Discuss those questions with your group and try to get a good understanding of what is happening.
-
-**Question 3:** Instead of arbitrary functions, we will now consider functions that are constant everywhere except on a finite domain. We represent such functions in the following way:
-
-```scala
-class FiniteFun[A](mappings: immutable.Map[A, A], default: A) {
- def apply(x: A): A =
- mappings.get(x) match {
- case Some(y) => y
- case None => default
- }
-
- def andThen(that: FiniteFun[A]): FiniteFun[A] = ???
-}
-```
-
-Implement the andThen method. Can pipelines of such finite functions be efficiently constructed in parallel using the appropriately modified `toPipeline` method? Can the resulting pipelines be efficiently executed?
-
-**Question 4:** Compare the *work* and *depth* of the following two functions, assuming infinite parallelism. For which kind of input would the parallel version be asymptotically faster?
-
-```scala
-def applyAllSeq[A](x: A, fs: Seq[FiniteFun[A]]): A = {
- // Applying each function sequentially.
- var y = x
- for (f <- fs)
- y = f(y)
- y
-}
-
-def applyAllPar[A](x: A, fs: ParSeq[FiniteFun[A]]): A =
- if (fs.isEmpty) x
- else {
- // Computing the composition in parallel.
- val p = fs.reduce(_ andThen _)
- // Applying the pipeline.
- p(x)
- }
-```
diff --git a/exercises/exercise-4.md b/exercises/exercise-4.md
deleted file mode 100644
index 5db6c4ef58a5e122b56cfb04bc05b86d8e3e7e93..0000000000000000000000000000000000000000
--- a/exercises/exercise-4.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# Exercise 4
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b exercise-4 git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git exercise-4
-```
-
-Update the README.md file with your solutions. Don't forget to list the group members's SCIPER numbers.
-
-# Problem 1: Implementing map and filter on Futures
-
-In this exercise, you will come up with an implementation of the `map` and `filter` methods of Futures. First of all, spend some time as a group to make sure that you understand what those methods are supposed to do. Then, complete the following code to implement the two methods:
-
-```scala
-trait Future[T] { self =>
- def map[S](f: T => S): Future[S] =
- new Future[S] {
- def onComplete(callback: Try[S] => Unit): Unit = ???
- }
-
- def filter(f: T => Boolean): Future[T] =
- new Future[T] {
- def onComplete(callback: Try[T] => Unit): Unit = ???
- }
-}
-```
-
-In the case of `filter`, if the original `Future` successfully returns a value which does not satisfy the predicate, the new `Future` should return a `Failure` containing a `NoSuchElementException`.
-
-# Problem 2: Coordinator / Worker
-
-In this exercise, you will have to implement a Coordinator / Worker actor system, in which one actor, the coordinator, dispatches work to other actors, the workers. Between the coordinator and the workers, only two kinds of messages are sent: `Request` and `Ready` messages.
-
-```scala
-case class Request(computation: => Unit)
-case object Ready
-```
-
-The coordinator actor sends `Request` messages to workers to request them to perform some computation (passed as an argument of `Request`). Upon reception of a `Request`, a worker should perform the computation. Workers should send a `Ready` message to their coordinator whenever they finish executing the requested computation, and also right after they are created.
-
-The coordinator actor itself receives requests through `Request` messages from clients. The coordinator actor should then dispatch the work to worker actors. The coordinator should however never send a request to a worker which has not declared itself ready via a `Ready` message beforehand.
-
-Implement the `Coordinator` and `Worker` classes.
-
-```scala
-class Coordinator extends Actor {
- ???
-
- override def receive = ???
-}
-
-class Worker(coordinator: Coordinator) extends Actor {
- ???
-
- override def receive = ???
-}
-```
-
-An example system using the Coordinator and Worker actors is shown below.
-
-```scala
-object Main extends App {
- val coordinatorProps: Props = Props(new Coordinator())
- def workerProps(coord: Coordinator): Props = Props(new Worker(coord))
-
- val system = ActorSystem("coordinator/worker")
-
- val coordinator = system.actorOf(coordinatorProps)
- val workers = Seq.fill(10) {
- system.actorOf(workerProps(coordinator))
- }
-
- // Now, clients should be able to send requests to the coordinator…
- coordinator ! Request(println(3 + 5))
- coordinator ! Request(println(67 * 3))
- // And so on...
-}
-```
-
-*Hint*: In order to fulfill its job, the coordinator should remember which workers are ready and what requests are still to be allocated to a worker.
diff --git a/exercises/exercise-5.md b/exercises/exercise-5.md
deleted file mode 100644
index 9fc1805eba679352588a4d9687c434d42a11cd59..0000000000000000000000000000000000000000
--- a/exercises/exercise-5.md
+++ /dev/null
@@ -1,153 +0,0 @@
-# Exercise 5
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b exercise-5 git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git exercise-5
-```
-
-Update the README.md file with your solutions. Don't forget to list the group members's SCIPER numbers.
-
-# Problem 1: Message Processing Semantics
-
-Consider the following actor system:
-
-```scala
-case class Write(value: Int)
-case class Read(requester: ActorRef)
-case class Answer(value: Int)
-
-class Memory extends Actor {
- var value = 0
-
- override def receive: Receive = {
- case Write(newValue) => value = newValue
- case Read(requester) => requester ! Answer(value)
- }
-}
-
-object Memory {
- def props: Props = Props(new Memory)
-}
-
-class Client1(memory: ActorRef) extends Actor {
- memory ! Write(1)
- memory ! Read(self)
-
- override def receive: Receive = {
- case Answer(value) => println(value)
- }
-}
-
-object Client1 {
- def props(memory: ActorRef): Props = Props(new Client1(memory))
-}
-
-object Example extends App {
- val system = ActorSystem("example")
-
- val memory = system.actorOf(Memory.props, "memory")
- val client = system.actorOf(Client1.props(memory), "client")
-}
-```
-
-What are the possible values printed by the `println` command in the `Client1` actor? Why?
-Now, consider the following system:
-
-```scala
-// Messages as before.
-
-// Memory class as before.
-
-class Proxy(memory: ActorRef) extends Actor {
- override def receive: Receive = {
- case message => memory ! message
- }
-}
-
-object Proxy {
- def props(memory: ActorRef): Props = Props(new Proxy(memory))
-}
-
-class Client2(memory: ActorRef, proxy: ActorRef) extends Actor {
-
- memory ! Write(1) // XXX
- proxy ! Read(self) // XXX
-
- override def receive: Receive = {
- case Answer(value) => println(value)
- }
-}
-
-object Client2 {
- def props(memory: ActorRef, proxy: ActorRef): Props =
- Props(new Client2(memory, proxy))
-}
-
-object Example extends App {
- val system = ActorSystem("example")
-
- val memory = system.actorOf(Memory.props, "memory")
- val proxy = system.actorOf(Proxy.props(memory), "proxy")
- val client = system.actorOf(Client2.props(memory, proxy), "client")
-}
-```
-
-What are the possible values printed by the `println` command in the `Client2` actor? Why? Would the output be different if the commands annotated with `XXX` were issued in the other order? What if both messages are sent through the `Proxy` actor?
-
-## Problem 2: The Josephus Problem
-
-In this exercise, we will revisit the famous *Josephus problem*. In this problem, a group of soldiers trapped by the enemy decide to commit suicide instead of surrendering. In order not to have to take their own lives, the soldiers devise a plan. All soldiers are arranged in a single circle. Each soldier, when it is their turn to act, has to kill the next soldier alive next to them in the clockwise direction. Then, the next soldier that is still alive in the same direction acts. This continues until there remains only one soldier in the circle. This last soldier is the lucky one, and can surrender if he decides to. The *Josephus problem* consists in finding the position in the circle of this lucky soldier, depending on the number of soldiers.
-
-In this exercise, you will implement a *simulation* of the mass killing of the soldiers. Each soldier will be modeled by an actor. Soldiers are arranged in a circle and when their turn comes to act, they kill the next alive soldier in the circle. The next soldier that is still alive in the circle should act next. The last soldier remaining alive does not kill himself but prints out its number to the standard output.
-
-The code on the next page covers the creation of all actors and the initialisation of the system. Your goal is to implement the `receive` method of the actors.
-
-*Hint:* Think about what state the soldier must have.
-
-```scala
-class Soldier(number: Int) extends Actor {
-
- // You goal is to implement this.
- def receive: Receive = ???
-}
-
-object Soldier {
- // The different messages that can be sent between the actors:
-
- // The recipient should die.
- case object Death
-
- // The recipient should update its next reference.
- case class Next(next: ActorRef)
-
- // The recipient should act.
- case object Act
-
- def props(number: Int): Props = Props(new Soldier(number))
-}
-
-
-object Simulation {
- // Initialization
- val system = ActorSystem("mySystem")
-
- def start(n: Int) {
- require(n >= 1)
-
- // Creation of the actors.
- val actors = Seq.tabulate(n) { (i: Int) =>
- system.actorOf(Soldier.props(i), "Soldier#" + i)
- }
-
- // Inform all actors of the next actor in the circle.
- for (i <- 0 to (n - 2)) {
- actors(i) ! Next(actors(i + 1))
- }
- actors(n - 1) ! Next(actors(0))
-
- // Inform the first actor to start acting.
- actors(0) ! Act
- }
-}
-```
diff --git a/exercises/exercise-6.md b/exercises/exercise-6.md
deleted file mode 100644
index cdf4140d23dad93a969d6d9b28240f421d3c03c8..0000000000000000000000000000000000000000
--- a/exercises/exercise-6.md
+++ /dev/null
@@ -1,110 +0,0 @@
-# Exercise 6
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b exercise-6 git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git exercise-6
-```
-
-Update the README.md file with your solutions. Don't forget to list the group members's SCIPER numbers.
-
-# Problem 1: Spark Fundamentals
-
-## Question 1
-
-Assume that you have the following RDD of strings:
-
-```scala
-val rdd: RDD[String] = ...
-```
-
-The RDD contains strings of space-separated words. Efficiently compute the total number of words in the RDD. In your solution, *identify the transformations and actions*.
-
-*Hint:* You may use the method `.split(separator: String)` on strings to split a string based on a separator.
-
-## Question 2
-
-Assume that you are given the following RDD of strings that holds raw logs (info, warning and error messages).
-
-```scala
-val rawLogs: RDD[String] = ...
-```
-
-In addition, assume that you are given the following methods:
-
-```scala
-def toLog(str: String): LogEntry = ...
-def isError(log: LogEntry): Boolean = ...
-def message(log: LogEntry): String = ...
-def isRecent(log: LogEntry): Boolean = ...
-```
-
-Write a code snippet that *efficiently* computes both of the following values:
-
-1. The number of errors in the logs.
-2. An array containing all the messages corresponding to *recent* errors.
-
-Make sure to avoid redundant computations.
-
-## Question 3
-
-Assume that you are given an RDD of strings called `rdd`. Explain the difference between the following two programs:
-
-1. rdd.collect().foreach(println)
-2. rdd.foreach(println)
-
-## Question 4
-
-Consider the following piece of code:
-
-```scala
-numbers.map(1 to _).map(_.sum).take(10)
-```
-
-Explain how the above code is executed if:
-1. `numbers` is a `List[Int]`
-2. `numbers` is a `RDD[Int]`
-
-# Problem 2: Demographics
-
-Imagine that you work in a large software company, whose main product is a social media website. In this exercise, your goal will be to compute demographical data from the dataset of all millions users of the website. You may assume that users' entries are of form Person. This data type is defined as:
-
-```scala
-case class Person(age: Int, name: String)
-```
-
-The RDD that holds all the entries is called `people`.
-
-## Question 1
-
-Your goal in this first part is to first exclude of the dataset all people that are under 18, and only keep the age.
-
-Then, you will have to count how many users fall in the following age groups:
-
-```
-1: 18-25
-2: 26-35
-3: 36-45
-4: 46-65
-5: 66+
-```
-
-You may safely assume that no user in your website is older than 200 years.
-
-For this question, we ask you to iterate through the filtered dataset 5 times – once for each group – and count the number of people in the group. Your goal is to end up with a list of the counts. You may assume that you are given the following list:
-
-```scala
-val groups = List((18, 25), (26, 35), (36, 45), (46, 65), (66, 200))
-```
-
-## Question 2
-
-Come up with a solution that does not need to iterate the dataset 5 times. You may make use of the following function in your solution:
-
-```scala
-def groupOf(age: Int): Int = ...
-```
-
-This function will return the number of the group associated to the age. For instance, it will return 2 for the age 27, and 4 for 46.
-
-As before, your goal will be to return a list of all counts. Which of the two versions do you think would be more efficient? Why?
diff --git a/exercises/exercise-7.md b/exercises/exercise-7.md
deleted file mode 100644
index 66b8dbab1ce3c12ffde842e099ace1477cd26159..0000000000000000000000000000000000000000
--- a/exercises/exercise-7.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# Exercise 7
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b exercise-7 git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git exercise-7
-```
-
-Update the README.md file with your solutions. Don't forget to list the group members's SCIPER numbers.
-
-You and your team are doing professional data consulting on a wide spectrum of projects. Last week, you've been given a call from a company called We Really Care Inc (short WRC Inc.) This company is one of the oldest health insurance companies in the world and they've aggregated insane amounts of data about their activity all across the globe.
-
-After countless months of trying to collect all of the bits and pieces into uniform data schema, you've managed to minimize all the relevant logs into a single well-formed data set that consists of four distributed collections:
-
-```scala
-case class Broker(brokerId: Int, fullName: String, ...)
-case class Client(clientId: Int, fullName: String, ...)
-case class Contract(contractId: Int, brokerId: Int, clientId: Int, planId: Int, ...)
-case class Plan(planId: Int, yearIntroducedIn: Int, price: Double, ...)
-
-val brokers: RDD[Broker] = ...
-val contracts: RDD[Contract] = ...
-val clients: RDD[Client] = ...
-val plans: RDD[Plan] = ...
-```
-
-The company employs brokers whose job is to seek and arrange contracts with clients. Contracts are tied to specific plans in the company's product line-up.
-
-## Question 1.1
-
-In the past year WRC has been having a hard time financially. Due to this, upper management decided to let some people go. To make an educated decision about whom to fire they need to analyze the performance of the current employees.
-
-### Part 1
-
-Compute an RDD that maps broker full names to their total revenue (i.e. total income from all of their contracts). Try to compute this as efficiently as possible.
-
-```scala
-val brokerNameRevenue: RDD[(String, Double)] = ???
-```
-
-### Part 2
-
-Efficiently compute an RDD that outputs the names of the 20% lowest-performing employees.
-
-```scala
-val low20: Array[String] = ???
-```
-
-## Question 1.2
-
-WRC is quite an old company and has a large list of life-long clients who managed to get a contract a long time ago, when everything was cheaper. Naturally, WRC doesn't benefit as much as it could from those clients.
-
-To optimize its revenue, WRC decided to do a proactive outreach to such clients and try to upsell them a newer, fancier plan that is actually just the same thing but more expensive.
-
-### Part 1
-
-Compute an RDD of client's full names whose oldest contract is older than 10 years old.
-
-```scala
-val upsellClientNames: RDD[String] = ???
-```
-
-### Part 2
-For every upsell client, find the cheapest plan of the current year that's still more expensive than the current one they have at the moment, if any.
-
-```scala
-val upsellSuggestion: RDD[(String, Plan)] = ???
-```
diff --git a/exercises/exercise-8.md b/exercises/exercise-8.md
deleted file mode 100644
index 85f311e5375e540c827a3a90323727da20bb5ed3..0000000000000000000000000000000000000000
--- a/exercises/exercise-8.md
+++ /dev/null
@@ -1,94 +0,0 @@
-## Exercise 8
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b exercise-8 git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git exercise-8
-```
-
-Update the README.md file with your solutions. Don't forget to list the group members's SCIPER numbers.
-
-# Problem 1: Federal Statistical Office
-
-You have been recently hired as a Spark consultant at the Federal Statistical Office in Neuchâtel. They have recently started using Spark for all their data processing tasks, but for some reason, they are not entirely satisfied with Spark thus far.
-
-Below is the datatype they use to record information about people living in Switzerland. The RDD that contains all records (several millions) is called people.
-
-```scala
-case class Person(age: Long, salary: Long, town: Int)
-val people: RDD[Person] = ???
-```
-
-The Spark cluster they have put in place consists of 8 identical machines, each of which has 4 cores.
-
-## Question 1.1
-
-On your very first day, you are immediately tasked with investigating some piece of code. Even though the statistician that wrote the code is absolutely sure it is correct, he is not satisfied with its performance! It's awfully slow!
-
-```scala
-people.groupBy(_.age).map {
- case (age, peopleOfAge) =>
- val salaries = peopleOfAge.map(_.salary)
- val n = peopleOfAge.size
- val mean = salaries.sum / n
- val variance = salaries.map(x => (x - mean) * (x - mean)).sum / n
-
- (age, (mean, Math.sqrt(variance).toLong))
-}
-```
-
-Using your own words, explain why the above code is supposed to do and why it is not as efficient as it could be. What happens?
-
-*Hint*: You may find it useful to draw a graphical representation of the nodes and show data exchanges between the nodes.
-
-## Question 1.2
-
-Rewrite the above piece of code to be as efficient as possible. In your solution, try to keep the communication overhead between the nodes as small as possible. Think about the location of your data on the different nodes.
-
-*Hint*: You may recall from your Probabilities and statistics course that there exist multiple formulas to compute the variance of a series x of n values with mean m. For instance:
-
-
-
-One of the two forms might be more appropriate in your solution.
-
-## Question 1.3
-
-Could the computation be done even faster if the data was already partitioned? Write the code to partition the data, using a `Partitioner`, to improve data locality.
-
-# Problem 2: Partitioners
-
-## Question 2.1
-
-What is a partitioner? Come up with *two reasons* why you would want to repartition data.
-
-## Question 2.2
-
-Which of the following transformations preserve the partitioner of the parent RDD, if any?
-
-- `map`
-- `mapValues`
-- `filter`
-- `flatMap`
-- `flatMapValues`
-- `join`
-- `reduceByKey`
-- `groupByKey`
-
-Within your group, discuss what makes it possible for some transformations to preserve and propagate the parent's partitioner.
-
-## Question 2.3
-
-Which of the following transformations will return an RDD with a partitioner, even when the parent doesn't have one?
-
-- `map`
-- `mapValues`
-- `filter`
-- `flatMap`
-- `flatMapValues`
-- `join`
-- `reduceByKey`
-- `groupByKey`
-
-## Question 2.4
-
-What is the difference between a `HashPartitioner` and a `RangePartitioner`? When would you use one over the other?
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diff --git a/exercises/solutions-1.md b/exercises/solutions-1.md
deleted file mode 100644
index 69ee5c54823c42fdccbf74b0ccc7029273de3c0f..0000000000000000000000000000000000000000
--- a/exercises/solutions-1.md
+++ /dev/null
@@ -1,98 +0,0 @@
-# Exercise 1 : Introduction to Concurrency
-
-## Question 1
-
-Yes
-
-## Question 2
-
-#### Property 1 holds
-
-Assuming that the execution of two concurrent threads only interleaves instructions and that reads and writes are executed atomically, it can be shown that property 1 always holds. Unfortunately, such strong guarantees are not offered by the Java Memory Model. If you are interested, have a look at the note below on the Java Memory Model.
-
-#### Violation of property 2
-
-Consider 2 threads that execute concurrently `transfer(from, to, amount)` with the exact same parameters. Assume that the account from has sufficient funds for at least one transfer.
-
-Thread 1 executes until it has computed the value balanceFrom - amount and then stops. Thread 2 then executes in its entirety the call to `transfer(from, to, amount)`. Then thread 1 resumes its execution and completes the call to `transfer`.
-
-At the end of this execution, the total amount of money held by the bank has changed. It has in fact increased by the value amount.
-
-#### Note on the Java Memory Model
-
-Assuming the Java Memory Model, both of the two properties can potentially be violated. Indeed, the model only ensures that the execution of each thread appears sequential to the thread itself, and not to any other concurrently running threads. Seemingly atomic instructions can be arbitrarily decomposed by the underlying virtual machine. Sequences of instructions can also be reordered at will by the VM, as long as the execution of a single thread appears as if it were executed sequentially. In these settings, both properties can be violated.
-
-## Question 3
-
-Variant 1
-
-In this variant, property 2 can be violated. It is not vulnerable to deadlocks.
-
-Variant 2
-
-In this variant, none of the two properties can be violated. However, it is susceptible to deadlocks.
-
-Variant 3
-
-In this last variant, none of the two properties can be violated and no deadlock can occur. It is however still not entirely satisfactory, since no two threads can execute transfers in parallel, even when the accounts are totally disjointed. Can you think of a better solution?
-
-## Question 1
-
-```scala
-def minMax(a: Array[Int]): (Int, Int) = {
- val threshold = 10
-
- def minMaxPar(from: Int, until: Int): (Int, Int) = {
-
- if (until - from <= threshold) {
- var i = from
- var min = a(from)
- var max = a(from)
-
- while (i < until) {
- val x = a(i)
- if(x < min) min = x
- if(x > max) max = x
- i = i + 1
- }
-
- (min, max)
- } else {
- val mid = from + ((until - from) / 2)
- val ((xMin, xMax),
- (yMin, yMax)) = parallel(minMaxPar(from, mid),
- minMaxPar(mid, until))
- (min(xMin, yMin), max(xMax, yMax))
- }
- }
-
- minMaxPar(0, a.size)
-}
-```
-
-## Question 2
-
-```scala
-def minMax(data: ParSeq[Int]): (Int, Int) = data.map({
- (x: Int) => (x, x)
-}).reduce({
- case ((mn1, mx1), (mn2, mx2)) => (min(mn1, mn2), max(mx1, mx2))
-})
-```
-
-Or:
-
-```scala
-def minMax(data: ParSeq[Int]): (Int, Int) =
- (data.reduce(min), data.reduce(max))
-```
-
-## Question 3
-
-The function `f` must be associative. That is, for any `x`, `y`, `z`, it should be the case that:
-
-```
-f(x, f(y, z)) == f(f(x, y), z).
-```
-
-Both the `min` and `max` functions are associative. In addition, it can be easily shown that pairwise application of associative functions is also associative. From this follows that `f` is indeed associative.
diff --git a/exercises/solutions-2.md b/exercises/solutions-2.md
deleted file mode 100644
index 99da85c42282b3217e738422bc1127fdeae98002..0000000000000000000000000000000000000000
--- a/exercises/solutions-2.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# Exercise 1 : Aggregate
-
-## Question 1
-
-- g(f(z, x1), f(f(z, x2), x3))
-- g(f(f(z, x1), x2), f(z, x3))
-- g(g(f(z, x1), f(z, x2)), f(z, x3))
-- g(f(z, x1), g(f(z, x2), f(z, x3)))
-
-## Question 2
-
-Variant 1
-
-This might lead to different results.
-
-Variant 2
-
-This might lead to different results.
-
-Variant 3
-
-This always leads to the same result.
-
-Variant 4
-
-This always leads to the same result.
-
-## Question 3
-
-A property that implies the correctness is:
-
-```
-forall xs, ys. g(xs.F, ys.F) == (xs ++ ys).F (split-invariance)
-```
-
-where we define
-
-```
-xs.F == xs.foldLeft(z)(f)
-```
-
-The intuition is the following. Take any computation tree for
-`xs.aggregate`. Such a tree has internal nodes labelled by g and segments processed using `foldLeft(z)(f)`. The split-invariance law above says that any internal g-node can be removed by concatenating the segments. By repeating this transformation, we obtain the entire result equals `xs.foldLeft(z)(f)`.
-
-The split-invariance condition uses `foldLeft`. The following two conditions together are a bit simpler and imply split-invariance:
-
-```
-forall u. g(u,z) == u (g-right-unit)
-forall u, v. g(u, f(v,x)) == f(g(u,v), x) (g-f-assoc)
-```
-
-Assume g-right-unit and g-f-assoc. We wish to prove split-invariance. We do so by induction on the length of `ys`. If ys has length zero, then `ys.foldLeft` gives `z`, so by g-right-unit both sides reduce to xs.foldLeft. Let `ys` have length `n>0` and assume by I.H. split-invariance holds for all `ys` of length strictly less than `n`. Let `ys == ys1 :+ y` (that is, y is the last element of `ys`). Then
-
-```
-g(xs.F, (ys1 :+ y).F) == (foldLeft definition)
-g(xs.F, f(ys1.F, y)) == (by g-f-assoc)
-f(g(xs.F, ys1.F), y) == (by I.H.)
-f((xs++ys1).F, y) == (foldLeft definition)
-((xs++ys1) :+ y).F == (properties of lists)
-(xs++(ys1 :+ y)).F
-```
-
-## Question 4
-
-```scala
-def aggregate[B](z: B)(f: (B, A) => B, g: (B, B) => B): B =
- if (this.isEmpty) z
- else this.map((x: A) => f(z, x)).reduce(g)
-```
-
-## Question 5
-
-```scala
-def aggregate(z: B)(f: (B, A) => B, g: (B, B) => B): B = {
-
- def go(s: Splitter[A]): B = {
- if (s.remaining <= THRESHOLD)
- s.foldLeft(z)(f)
- else {
- val splitted = s.split
-
- val subs = splitted.map((t: Splitter[A]) => task { go(t) })
- subs.map(_.join()).reduce(g)
- }
- }
-
- go(splitter)
-}
-```
-
-## Question 6
-
-The version from question 4 may require 2 traversals (one for `map`, one for `reduce`) and does not benefit from the (potentially faster) sequential operator `f`.
-
-# Exercise 2 : Depth
-
-## Question 1
-
-Somewhat counterintuitively, the property doesn't hold. To show this, let's take the following values for *L1*, *L2*, *T*, *c*, and *d*.
-
-```
-L1 = 10, L2 = 12, T = 11, c = 1, and d = 1.
-```
-
-Using those values, we get that:
-
-```
-D(L1) = 10
-D(L2) = max(D(6), D(6)) + 1 = 7
-```
-
-## Question 2
-
-*Proof sketch*
-
-Define the following function D'(L).
-
-
-
-Show that *D(L) ≤ D'(L)* for all *1 ≤ L*.
-
-Then, show that, for any *1 ≤ L1 ≤ L2* we have *D'(L1) ≤ D'(L2)*. This property can be shown by induction on *L2*.
-
-Finally, let *n* be such that *L ≤ 2n < 2L*. We have that:
-
-```
-D(L) ≤ D'(L) Proven earlier.
- ≤ D'(2n) Also proven earlier.
- ≤ log2(2n) (d + cT) + cT
- < log2(2L) (d + cT) + cT
- = log2(L) (d + cT) + log2(2) (d + cT) + cT
- = log2(L) (d + cT) + d + 2cT
-```
-
-Done.
diff --git a/exercises/solutions-3.md b/exercises/solutions-3.md
deleted file mode 100644
index f0715a0ebdd72e89d0df973a022448273524de86..0000000000000000000000000000000000000000
--- a/exercises/solutions-3.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Exercise 1 : Parallel Encoding
-
-```scala
-def rle(data: ParSeq[Char]): Buffer[(Char, Int)] = {
-
- def g(as: Buffer[(Char, Int)], bs: Buffer[(Char, Int)]) =
- if (as.isEmpty || bs.isEmpty || as.last._1 != bs.head._1)
- as ++ bs
- else
-as.init.append((as.last._1, as.last._2 + bs.head._2)) ++ bs.tail
-
- def f(acc: Buffer[(Char, Int)], x: Char) =
- if (acc.isEmpty || acc.last._1 != x)
- acc.append((x, 1))
- else
- acc.init.append((x, acc.last._2 + 1))
-
- val z: Buffer[(Char, Int)] = Buffer.empty
-
- data.aggregate(z)(f, g)
-}
-```
-
-# Exercise 2 : Parallel Two Phase Construction
-
-
-```scala
-class DLLCombiner[A] extends Combiner[A, Array[A]] {
- var head: Node[A] = null
- var last: Node[A] = null
- var size: Int = 0
-
- override def +=(elem: A): Unit = {
- val node = new Node(elem)
- if (size == 0) {
- head = node
- last = node
- size = 1
- }
- else {
- last.next = node
- node.previous = last
- last = node
- size += 1
- }
- }
- override def combine(that: DLLCombiner[A]): DLLCombiner[A] =
- if (this.size == 0)
- that
- else if (that.size == 0)
- this
- else {
- this.last.next = that.head
- that.head.previous = this.last
-
- this.size = this.size + that.size
- this.last = that.last
-
- this
- }
-
- // This is not implemented in parallel yet.
- override def result(): Array[A] = {
- val data = new Array[A](size)
-
- var current = head
- var i = 0
- while (i < size) {
- data(i) = current.value
- i += 1
- current = current.next
- }
- data
- }
-}
-```
-
-## Question 1
-
-The complexity of `+=` is constant, as well as the complexity of `combine`. This is obviously well within the desired complexity range. The result function takes time linear in the size of the data, which is acceptable according to the Combiner requirements. However, the result function should work in parallel according to the contract. This isn't the case here.
-
-
-## Question 2
-
-The idea is to copy the double linked list to the array from both ends at the same time. For this, we create a task that handles the second half of the array, while the current thread copied the first half.
-
-```scala
-override def result(): Array[A] = {
- val data = new Array[A](size)
- val mid = size / 2
-
- // This is executed on a different thread.
- val taskEnd = task {
- var i = size - 1
- var current = last
- while (i >= mid) {
- data(i) = current.value
- current = current.previous
- i -= 1
- }
- }
-
- // This is executed on the current thread.
- var i = 0
- var current = head
- while (i < mid) {
- data(i) = current.value
- current = current.next
- i += 1
- }
- taskEnd.join()
- data
-}
-```
-
-## Question 3
-
-The actual answer to this question is: *it depends*. Two see why, we first make the following observation:
-
-All implementations of the result function must consist of primarily two operations:
-1. Moving to the next node in the list, and,
-2. Copying the value of the node to the array.
-
-Depending on the actual cost of the two operations, one may devise schemes that can make efficient use of more than two threads. For instance, assume for a moment that copying a value to the array is significantly costlier than moving to the next node in the list. In this case, we could execute the function efficiently in parallel by spawning multiple threads starting from the head of the list, each handling a disjoint set of indexes (for instance, one thread takes indexes of the form 4n, another 4n + 1 and so on).
-
-On the other hand, if we assume that moving to the next node in the list has a cost comparable to the one of copying a value to the array, then finding such a strategy is more challenging, or even impossible.
-
-However, there are ways to circumvent this problem by modifying the data structure used. One way could be to keep track of the middle of the double linked lists. The result function could then execute in parallel on 4 different threads by copying the array from both ends and from the middle (in both directions) simultaneously. The problem would then be to efficiently maintain the pointer to the middle of the list, which might not be a trivial task when combining arbitrary lists together. If you are interested in learning more about such data-structures, we encourage you to look up the skip list data structure, which generalises on this idea.
-
-Another solution would be to modify the nodes so that they also point to their successor's successor and their predecessor's predecessor. This way, two threads could start from the start of the list and two from the end. In each case, one thread would be responsible for odd indexes and the other for even ones. This solution does not change at all the complexity of the various Combiner operations, but requires a bit more bookkeeping.
-
-
-# Exercise 3: Pipelines
-
-## Question 1
-
-```scala
-def toPipeline(fs: ParSeq[A => A]): A => A =
- if (fs.isEmpty)
- (x: A) => x
- else
- fs.reduce(_ andThen _)
-```
-
-## Question 2
-
-Even though the pipeline is constructed in parallel, *it will not itself execute in parallel*. The resulting pipeline still has to apply its argument to all the functions it contains sequentially. This is due to the fact that the andThen method simply returns a function that will apply the first function and then the second, sequentially.
-
-## Question 3
-
-```scala
-def andThen(that: FiniteFun[A]): FiniteFun[A] = {
- val newDefault = that(default)
-
- val newMappings = for {
- (x, y) <- mappings
- val z = that(y)
- if (z != newDefault)
- } yield (x, z)
-
- new FiniteFun(newMappings, newDefault)
-}
-```
-
-Pipelines of such functions can be efficiently constructed in parallel, as was the case for "normal" functions also. Also, interestingly, the resulting pipeline can be executed efficiently. The execution time of the pipeline does not depend on the number of functions in the pipeline, only on the lookup time in the finite map mappings (which can be nearly constant time if the underlying map is a hashtable). The size of this map is upper bounded by the size of the mappings of the functions in the pipeline.
-
-## Question 4
-
-To simplify the analysis, we will assume that lookup in the mappings takes constant time, and thus that applying a FiniteFun also takes constant time. Let's also assume that fs is of size `n` for both functions.
-
-Since the function is purely sequential, the work and depth of applyAllSeq are equal. They amount to `n` applications of a finite function, which is linear in `n`.
-
-For applyAllPar, things are a bit more complex. Let's denote by `d` the size of the largest domain of all functions passed as argument.
-
-The depth of the function is simply the depth of computing the pipeline (`fs.reduce(_ andThen _)`) plus a constant for applying the pipeline. Assuming infinite parallelism, this results in a depth that is in `O(log2(n) ⋅ d)`.
-
-The work of applyAllPar is significantly more than its depth, and can be upper bounded by `O(n ⋅ d)`. Indeed, there are `n` applications of the `andThen` method, each of which takes `O(d)` time.
-
-When `d` is a constant, then the parallel version will be asymptotically faster than its sequential counterpart. If `d` is exponentially larger than `n`, then the sequential version is expected to perform better.
diff --git a/exercises/solutions-4.md b/exercises/solutions-4.md
deleted file mode 100644
index 9cf9274ca35a428965130418f81819ecd6fd0a2d..0000000000000000000000000000000000000000
--- a/exercises/solutions-4.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Exercise 1 : Implementing map and filter on Futures
-
-```scala
-trait Future[T] { self =>
- def map[S](f: T => S): Future[S] =
- new Future[S] {
- def onComplete(callback: Try[S] => Unit): Unit = self.onComplete {
- case Success(v) => callback(Success(f(v)))
- case Failure(e) => callback(Failure(e))
- }
- }
-
- def filter(f: T => Boolean): Future[T] =
- new Future[T] {
- def onComplete(callback: Try[T] => Unit): Unit = self.onComplete {
- case Success(v) =>
- if (f(v)) callback(Success(v))
- else callback(Failure(new NoSuchElementException("...")))
- case Failure(e) => callback(Failure(e))
- }
- }
-}
-```
-
-# Exercise 2 : Coordinator / Worker
-
-```scala
-class Coordinator extends Actor {
- var availableWorkers: List[ActorRef] = Nil
- var pendingRequests: List[Request] = Nil
-
- def receive: Receive = {
- case Ready =>
- if (pendingRequests.isEmpty) {
- availableWorkers = availableWorkers :+ sender
- }
- else {
- val request = pendingRequests.head
- pendingRequests = pendingRequests.tail
- sender ! request
- }
- case request: Request => availableWorkers match {
- case worker :: rest =>
- worker ! request
- availableWorkers = rest
- case Nil =>
- pendingRequests = pendingRequests :+ request
- }
- }
-}
-
-class Worker(coordinator: ActorRef) extends Actor {
- coordinator ! Ready
-
- def receive: Receive = {
- case Request(f) =>
- f()
- coordinator ! Ready
- }
-}
-```
diff --git a/exercises/solutions-5.md b/exercises/solutions-5.md
deleted file mode 100644
index bc65596b48a279b447b50bd64480bb05321ce7e9..0000000000000000000000000000000000000000
--- a/exercises/solutions-5.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# Exercise 1 : Message Processing Semantics
-
-For the `Client1` actor, the only possible output is 1. The reason is that messages between two actors are guaranteed to be received in the order they were sent.
-
-For the `Client2` actor, either 0 or 1 can be printed. There are no restrictions on the order in which messages are processed in this case. It might be the case that the `Memory` actor receives the `Write` message from the `Client2` first, or the Read message from the `Proxy` first. The order in which the messages are sent by the `Client2` doesn't change the possible behaviours of the system. In the case both messages are sent through the `Proxy`, then the only possible output is 1, since in this case the messages between the `Client2` and `Proxy`, as well as between `Proxy` and `Memory`, are guaranteed to be handled in the same order they were sent.
-
-## Exercise 2 : The Josephus Problem
-
-```scala
-import akka.actor._
-
-class Soldier(number: Int) extends Actor {
-
- import Soldier._
-
- def receive: Receive = behavior(None, None, false)
-
- def behavior(next: Option[ActorRef],
- killer: Option[ActorRef],
- mustAct: Boolean): Receive = {
-
- case Death => next match {
- case Some(myNext) =>
- sender ! Next(myNext)
- myNext ! Act
- println("Soldier " + number + " dies.")
- self ! PoisonPill
-
- case None =>
- context.become(behavior(
- next = None,
- killer = Some(sender),
- mustAct = mustAct))
- }
-
- case Next(newNext) =>
- if (newNext == self) {
- println("Soldier " + number + " is last !")
- } else if (!killer.isEmpty) {
- killer.get ! Next(newNext)
- newNext ! Act
- println("Soldier " + number + " dies.")
- self ! PoisonPill
- } else if (mustAct) {
- newNext ! Death
- context.become(behavior(
- next = None,
- killer = None,
- mustAct = false))
- } else {
- context.become(behavior(
- next = Some(newNext),
- killer = None,
- mustAct = false))
- }
- }
-
-
- case Act => next match {
- case Some(myNext) =>
- myNext ! Death
- context.become(behavior(
- next = None,
- killer = killer,
- mustAct = false))
-
- case None =>
- context.become(behavior(
- next = None,
- killer = killer,
- mustAct = true))
- }
- }
-}
-
-
-object Soldier {
- // The different messages that can be sent between the actors:
-
- // The recipient should die.
- case object Death
-
- // The recipient should update its next reference.
- case class Next(next: ActorRef)
-
- // The recipient should act.
- case object Act
-
- def props(number: Int): Props = Props(new Soldier(number))
-}
-
-
-object Simulation {
-
- import Soldier._
-
- // Initialization
- val system = ActorSystem("mySystem")
-
- def start(n: Int) {
- require(n >= 1)
-
- // Creation of the actors.
- val actors = Seq.tabulate(n) { (i: Int) =>
- system.actorOf(Soldier.props(i), "Soldier" + i)
- }
-
- // Inform all actors of the next actor in the circle.
- for (i <- 0 to (n - 2)) {
- actors(i) ! Next(actors(i + 1))
- }
- actors(n - 1) ! Next(actors(0))
-
- // Inform the first actor to start acting.
- actors(0) ! Act
- }
-}
-```
diff --git a/exercises/solutions-6.md b/exercises/solutions-6.md
deleted file mode 100644
index 157961f379dfbedf9199aa20bcffd364b2e0e447..0000000000000000000000000000000000000000
--- a/exercises/solutions-6.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# Exercise 1 : Spark Fundamentals
-
-## Question 1
-
-```scala
-rdd.map(text => text.split(" ").length).reduce(_ + _)
-```
-
-## Question 2
-
-```scala
-val errorLogs = rawLogs.map(toLog(_)).filter(isError(_)).cache()
-val numberErrors = errorLogs.count()
-val messages = errorLogs.filter(isRecent).map(message(_)).collect()
-```
-
-## Question 3
-
-In the first case, all the strings are displayed on the master. In the second case, records are displayed on the standard output of the worker nodes.
-
-## Question 4
-
-In the case of 2., the map operations are pipelined and only applied to 10 elements.
-
-# Exercise 2 : Demographics
-
-
-## Question 1
-
-```scala
-val adultAges = people.map(_.age).filter(_ >= 18).cache()
-groups.map {
- case (lower, upper) => adultAges.filter { (age: Int) =>
- lower <= age && age <= upper
- }.count()
-}
-```
-
-## Question 2
-
-```scala
-val groups: Array[(Int, Int)] =
- people.map(_.age)
- .filter(_ >= 18)
- .map((age: Int) => (groupOf(age), 1))
- .reduceByKey(_ + _)
- .collect()
-
-// Now, we have a small collection
-// and can work on a single machine.
-groups.sortBy(_._1)
- .map(_._2)
- .toList()
-```
diff --git a/exercises/solutions-7.md b/exercises/solutions-7.md
deleted file mode 100644
index 99fa98384937332462498045f6d2504eb7e4f823..0000000000000000000000000000000000000000
--- a/exercises/solutions-7.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Exercise 1
-
-## Question 1.1
-
-### Part 1
-
-```scala
-val planContracts: RDD[(Int, Int)] =
- contracts.map(c => (c.planId, c.contractId))
-val planPrices: RDD[(Int, Double)] =
- plans.map(p => (p.planId, p.price))
-val contractPrices: RDD[(Int, Double)] =
- planContracts.join(planPrices).values
-val contractBrokers: RDD[(Int, Int)] =
- contracts.map(c => (c.contractId, c.brokerId))
-val brokerContractPrices: RDD[(Int, Double)] =
- contractBrokers.join(contractPrices).values
-val brokerIdRevenues: RDD[(Int, Double)] =
- brokerContractPrices.reduceByKey(_ + _)
-val brokerNames: RDD[(Int, String)] =
- brokers.map(b => (b.brokerId, b.fullName))
-val brokerNameRevenues: RDD[(String, Double)] =
- brokerNames.leftOuterJoin(brokerIdRevenues)
- .values
- .mapValues(_.getOrElse(0.0))
-```
-
-### Part 2
-
-```scala
-val low20 = brokerNameRevenue
- .top((brokers.count * 0.2).toInt)(Ordering.by(_._2).reverse)
-```
-
-## Question 1.2
-
-### Part 1
-
-```scala
-val planYear: RDD[(Int, Int)] =
- plans.map(p => (p.planId, p.yearIntroducedIn))
-val planClient: RDD[(Int, Int)] =
- contracts.map(c => (c.planId, p.clientId))
-val clientYear: RDD[(Int, Int)] =
- planClient.join(planYear).values
-val clientLatestYear: RDD[(Int, Int)] =
- clientYear.reduceByKey(_ max _)
-val clientNames: RDD[(Int, String)] =
- clients.map(c => (c.clientId, c.fullName))
-val upsellClientNames: RDD[String] =
- clientLatestYear.filter(p => 2019 - p._2 > 10)
- .join(clientNames)
- .values
-```
-
-### Part 2
-
-```scala
-val contractClient: RDD[(Int, Int)] = ... // similar to above
-val contractPlan: RDD[(Int, Plan)] =
- contracts.map(c => (c.planId, c.contractId))
- .join(plans.map(p => (p.planId, p)))
- .values
-val clientPlan: RDD[(Int, Plan)] =
- contractClient.join(contractPlan).values
-val clientUpsells: RDD[(Int, Plan)] =
- clientLatestPlan.cartesian(plans).filter {
- case ((clientId, clientPlan), newPlan) =>
- newPlan.price > clientPlan.price
- }.map {
- case ((clientId, clientPlan), newPlan) =>
- (clientId, newPlan)
- }
-val clientCheapeastUpsell: RDD[(Int, Plan)] =
- clientUpsells.reduceByKey {
- case (p1, p2) if p1.price < p2.price => p1
- case (_, p2) => p2
- }
-val clientNames: RDD[(Int, String)] =
- clients.map(c => (c.clientId, c.fullName))
-val upsellSuggestion: RDD[(String, Plan)] =
- clientCheapeastUpsell.join(clientNames).values
-```
diff --git a/exercises/solutions-8.md b/exercises/solutions-8.md
deleted file mode 100644
index 7fb4c1d4526f2c687a975752dc999b9714f9b8c0..0000000000000000000000000000000000000000
--- a/exercises/solutions-8.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# Exercise 1 : Federal Statistical Office
-
-## Question 1.1
-
-A lot of unnecessary communication happens on the cluster. Data is moved over the network from nodes to other nodes, which is called "shuffling". Network communication is extremely slow relatively to other operations.
-
-## Question 1.2
-
-```scala
-people.map(p => (p.age, p.salary))
- .mapValues(s => (1, s, s * s))
- .reduceByKey {
- case ((n1, s1, ss1), (n2, s2, ss2)) =>
- (n1 + n2, s1 + s2, ss1 + ss2)
- }
- .mapValues {
- case (n, sum, sumSquares) =>
- val mean = sum / n
- val variance = sumSquares / n - mean * mean
- (mean, Math.sqrt(variance).toLong)
- }
-```
-
-Note the use of `reduceByKey` instead of `groupByKey`. Also, `mapValues` is used whenever possible, which preserves the partitioner, if any. This will be useful later on.
-
-To see the difference in running time between `reduceByKey` and `groupByKey`, you may have a look at the following talk: https://www.youtube.com/watch?v=0KGGa9qX9nw (slides: https://www.slidesearch.net/slide/beyond-shuffling-scala-days-berlin-2016).
-
-
-## Question 1.3
-
-```scala
-// Code to partition the data before processing
-val pairs = people.map(p => (p.age, p.salary))
-val nPartitions = 32 // 8 machines, 4 cores each
-val tunedPartitioner = new RangePartitioner(nPartitions, pairs)
-val partitioned = pairs.partitionBy(tunedPartitioner).persist()
-
-
-// NOTE: Once partitioned, we can compute mean and variance as before,
-// using the partitioned RDD.
-partitioned.mapValues(s => (1, s, s * s))
- .reduceByKey {
- case ((n1, s1, ss1), (n2, s2, ss2)) =>
- (n1 + n2, s1 + s2, ss1 + ss2)
- }
- .mapValues {
- case (n, sum, sumSquares) =>
- val mean = sum / n
- val variance = sumSquares / n - mean * mean
- (mean, Math.sqrt(variance).toLong)
- }
-```
-
-# Exercise 2 : Partitioners
-
-## Question 2.1
-
-Partitioners specify which keys are hosted by the different partitions. Repartitioning in useful for example for:
-
-1. Improving data locality, and thus avoiding network shuffles.
-2. Balance the work between the different partitions.
-
-## Question 2.2
-
-The transformations that preserve partitioners are:
-
-- `mapValues`
-- `filter`
-- `flatMapValues`
-- `join`
-- `reduceByKey`
-- `groupByKey`
-
-(`map` and `flatMap` do not preserve partitioners)
-
-The partitioner can be preserved because the set of keys held by a partition in the resulting RDD is a subsets of the keys the partition held in the parent RDD. Therefore the partitioner still faithfully describes where the different keys are held.
-
-## Question 2.3
-
-The transformations that introduce partitioners are:
-
-- `join`
-- `reduceByKey`
-- `groupByKey`
-
-(`map`, `mapValues`, `filter`, `flatMap` and `flatMapValues` do not introduce partitioners)
-
-## Question 2.4
-
-A `HashPartitioner` partitions the data according to the hashcode of the key, while a `RangePartitioner` partitions the data according to an ordering on the keys.
-
-`HashPartitioners` generally split the work evenly between the different partitions. This partitioners does not require a specific ordering to exist on the keys.
-
-`RangePartitioners` allow grouping keys in the same range on the same partition. This can be useful to further improve data locality. In some cases, as seen in the video lecture, it also allows for better work balancing.
diff --git a/labs/lab2-reductions-and-prefix-sums/README.md b/labs/lab2-reductions-and-prefix-sums/README.md
deleted file mode 100755
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-# Reductions and Prefix Sums
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b reductions git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-reductions
-```
-
-## Useful links
-
- * [A guide to the Scala parallel collections](https://docs.scala-lang.org/overviews/parallel-collections/overview.html)
- * [The API documentation of the Scala parallel collections](https://www.javadoc.io/doc/org.scala-lang.modules/scala-parallel-collections_2.13/latest/scala/collection/index.html)
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-In this assignment, you will implement several variants of reduction and prefix
-sum algorithms. Each of the three parts of the assignment will exercise a
-different aspect of parallel programming:
-
-- choosing the right parallelization threshold
-- identifying the correct reduction operator
-- identifying the correct prefix sum operator
-
-We will use the `parallel` construct, defined in the file `package.scala`, as in the lecture to start parallel
-computations. Every `parallel` construct invocation takes two tasks as input and
-outputs the corresponding results as a tuple of two elements. You are not allowed
-to use the `task` construct in this assignment.
-
-## Parallel Counting Change
-
-If you took the course Functional Programming in Scala, you surely recall the
-assignment in which you had to count the number of ways in which you can make
-the change for a given amount of money.
-The text of that assignment was as follows:
-
-> Write a recursive function that counts how many different ways you can
-> make change for an amount, given a list of coin denominations. For
-> example, there are 3 ways to give change for 4 if you have coins with
-> denomination 1 and 2: 1+1+1+1, 1+1+2, 2+2.
-
-In this assignment, you will repeat the same task, but this time, your
-implementation will be parallel.
-Start with the sequential version of this problem once more -- the `countChange`
-function takes the amount of money and the list of different coin denominations.
-It returns the total number of different ways you can give change:
-
-```scala
-def countChange(money: Int, coins: List[Int]): Int
-```
-
-Note that the solution to this problem is recursive.
-In every recursive call, we either decide to continue subtracting the next coin
-in the `coins` list from the `money` amount, or we decide to drop the coin from the list of coins.
-For example, if we have 4 CHF, and coin denominations of 1 and 2, the call
-graph, in which every node depicts one invocation of the `countChange` method,
-is as follows:
-
- 4,[1, 2]
- 3,[1, 2] + 4,[2]
- 2,[1, 2] + 3,[2] 2,[2] + 4,[]
- 1,[1, 2] + 2,[2] 1,[2] + 3,[] 0,[2] + 2,[] 0
- 0,[1, 2] + 1,[2] 1 0 0 1 0
- 1 0
-
-We can take advantage of this recursive structure by evaluating different
-subtrees in parallel.
-This is the next part of the assignment -- implement the method `parCountChange`
-that counts the amount of change in parallel:
-
-```scala
-def parCountChange(money: Int, coins: List[Int], threshold: Threshold): Int
-```
-
-As we learned in the lectures, the `parCountChange` should not spawn parallel
-computations after reaching the leaf in the call graph -- the synchronization
-costs of doing this are way too high.
-Instead, we need to *agglomerate* parts of the computation.
-We do this by calling the sequential `countChange` method when we decide that
-the amount of work is lower than a certain value, called the *threshold*.
-To separate the concern of deciding on the threshold value from the
-implementation of our parallel algorithm, we implement the threshold
-functionality in a separate function, described by the `Threshold` type alias:
-
-```scala
-type Threshold = (Int, List[Int]) => Boolean
-```
-
-When a `threshold` function returns `true` for a given amount of money and the
-given list of coins, the sequential `countChange` implementation must be called.
-
-Implement `parCountChange`!
-
-Now that we have the `parCountChange` method, we ask ourselves what is the right
-implementation of the `threshold` function?
-Recall the examples from the lectures, such as summing the array values and
-computing the norm, where this was easy -- we exactly knew the amount of work
-required to traverse a subrange of the array, so `threshold` could return `true`
-when the length of the subrange was smaller than a certain value.
-
-Sadly, the total amount of work for a given `parCountChange` invocation is hard
-to evaluate from the remaining amount of money and a list of coins.
-In fact, the amount of work directly corresponds to the count that
-`parCountChange` returns, which is the value that we are trying to compute.
-Counting change is a canonical example of a task-parallel problem in which the
-partitioning the workload across processors is *solution-driven* -- to know how
-to optimally partition the work, we would first need to solve the problem
-itself.
-
-For this reason, many parallel algorithms in practice rely on heuristics to
-assess the amount of work in a subtask.
-We will implement several such heuristics in this exercise, and assess the
-effect on performance.
-First, implement the `moneyThreshold` method, which creates a threshold function
-that returns `true` when the amount of money is less than or equal to `2 / 3` of
-the starting amount:
-
-```scala
-def moneyThreshold(startingMoney: Int): Threshold
-```
-
-Remember that `a / b` will return the integer division of `a` and `b` when both
-operands are `Int`s. To avoid this problem, be sure to always do the multiplication of
-`startingMoney` by `2` before doing the division by `3`.
-
-Now run the `ParallelCountChange` application in sbt and observe the speedup:
-
-```
-> runMain reductions.ParallelCountChangeRunner
-```
-
-The previous heuristic did not take into account how many coins were left on the
-coins list, so try two other heuristics.
-Implement the method `totalCoinsThreshold`, which returns a threshold function
-that returns `true` when the number of coins is less than or equal to the `2 / 3` of the
-initial number of coins:
-
-```scala
-def totalCoinsThreshold(totalCoins: Int): Threshold
-```
-
-Again, be careful about the order of operations.
-
-Then, implement the method `combinedThreshold`, which returns a threshold
-function that returns `true` when the amount of money multiplied with the number
-of remaining coins is less than or equal to the starting money multiplied with
-the initial number of coins divided by `2`:
-
-```scala
-def combinedThreshold(startingMoney: Int, allCoins: List[Int]): Threshold
-```
-
-Which of the three threshold heuristics gives the best speedup?
-Can you think of a heuristic that improves performance even more?
-
-
-## Parallel Parentheses Balancing
-
-In this part of the assignment, we recall the Parenthesis Balancing assignment
-that might be familiar to you from the Functional Programming in Scala course.
-Here, the task is to, given an array of characters, decide if the parentheses in
-the array are balanced.
-
-Let us recall a few examples of strings in which parentheses are correctly
-balanced:
-
-```
-(if (zero? x) max (/ 1 x))
-I told him (that it's not (yet) done). (But he wasn't listening)
-```
-
-Similarly, the parentheses in the following strings are not balanced:
-
-```
-(o_()
-:-)
-())(
-```
-
-Implement a sequential function `balance`, which returns `true` iff the
-parentheses in the array are balanced:
-
-```scala
-def balance(chars: Array[Char]): Boolean
-```
-
-Next, you will implement a parallel version of this method.
-By now, you're already an expert at implementing the structure of a reduction
-algorithm, so you should have no problem there.
-The tricky part in parallel parentheses balancing is choosing the reduction
-operator -- you probably implemented `balance` by keeping an integer
-accumulator, incrementing it for left parentheses and decrementing it for the
-right ones, taking care that this accumulator does not drop below zero.
-Parallel parentheses balancing will require a bit more ingenuity on your part,
-so we will give you a hint -- you will need two integer values for the
-accumulator.
-
-Implement the `parBalance` method, which checks if the parentheses in the input
-array are balanced using two helper methods `reduce` and `traverse`.
-These methods implement the parallel reduction and the sequential traversal
-part, respectively:
-
-```scala
-def parBalance(chars: Array[Char], threshold: Int): Boolean = {
- def traverse(idx: Int, until: Int, _???_: Int, _???_: Int): ???
-
- def reduce(from: Int, until: Int): ??? = ???
-
- reduce(0, chars.length) == ???
-}
-```
-
-In this case, we again use the fixed threshold parameter, as we did in the
-lectures. Sections with size smaller or equal to the threshold should be processed sequentially.
-For maximum performance, use a `while` loop in the `traverse` method, or make
-`traverse` tail-recursive -- do not use a `Range`.
-
-Now, run the `ParallelParenthesesBalancing` application from sbt:
-
-```
-> runMain reductions.ParallelParenthesesBalancingRunner
-```
-
-How large was your speedup?
-
-If you are looking for additional challenges, prove that your reduction operator
-is associative!
-
-
-## Line of Sight
-
-In the last part of the exercise, you will be implementing an entirely new
-parallel algorithm -- you will apply the prefix sum algorithm to computing the
-line-of-sight in two-dimensional terrain.
-
-Imagine that you are standing at the zero of a coordinate system.
-The curve to your right describes the terrain that you are facing.
-This is shown in the following figure:
-
-
-
-The task of the line-of-sight algorithm is to compute the visibility of each
-point of the terrain, as shown in the following figure, where the visible area
-is above of the full line, and the obscured terrain is shown with a dotted
-line.
-
-
-
-What is the necessary and sufficient condition for a point on the terrain to be
-visibile from the zero of the coordinate system, where you are standing?
-Imagine that the terrain heights are represented with an array of numbers.
-We can compute (the tangent of) the viewing angle of each point on the terrain by dividing the
-height of the terrain `xs(i)` with the distance from the viewing point `i`,
-as shown in the following figure:
-
-
-
-It turns out that if the viewing angle of some point B is **lower** than the
-viewing angle of an earlier point A, then the point B is not visible, as shown
-in the following figure:
-
-
-
-This simple realization allows us to easily compute the line-of-sight on the
-terrain -- if you were a sequential programmer, you would traverse the array of
-height values from the beginning to the end, and write the maximum angle seen so
-far into the output array.
-
-Implement the sequential `lineOfSight` method, which, for each height entry in
-the `input` array (except for input(0) which is the location of the observer and
-is always zero), writes the maximum angle until that point into the `output`
-array (output(0) should be 0):
-
-```scala
-def lineOfSight(input: Array[Float], output: Array[Float]): Unit
-```
-
-We keep things simple -- instead of outputting an array of booleans denoting the
-visibilities, we only output the angles.
-
-Note that what we call an angle in this assignment is actually the tangent
-of the angle. Indeed, `xs(i)` is the opposing side of the angle and `i` the adjacent side.
-The ratio `xs(i) / i` that you compute is in fact the tangent of the angle!
-Since the tangent of an angle is strictly increasing between
-0° and 90°, it is a perfectly good replacement for the actual angle in our use case.
-Keep this in mind and make sure that you do not apply any trigonometic functions on the tangent!
-
-When we see a sequential algorithm that produces a sequence of values by
-traversing the input from left to right, this is an indication that the
-algorithm might have a parallel prefix sum variant.
-So let's try to implement one!
-
-Recall what you learned in the lectures -- the first phase of the parallel
-prefix sum algorithm is the *upsweep* phase.
-Here, the algorithm constructs the reduction tree by traversing parts of the
-input array in parallel.
-Implement the method `upsweepSequential`, which returns the maximum angle in a
-given part of the array, and the method `upsweep`, which returns the reduction
-tree over parts of the input array. If the length of the given part of the input
-array is less than or equal to `threshold`, then `upsweep` calls `upsweepSequential`.
-Note that the part of the input array that needs to traversed is represented
-using indices 'from' (inclusive) and 'until' (or 'end') (exclusive).
-
-```scala
-def upsweepSequential(input: Array[Float], from: Int, until: Int): Float
-```
-
-```scala
-def upsweep(input: Array[Float], from: Int, end: Int, threshold: Int): Tree
-```
-
-The `Tree` data type is either a `Leaf` or an inner `Node`, and it contains the
-maximum angle in the corresponding part of the array.
-Note that when the number of elements in a part of the input array,
-which is `(end - from)`, is smaller or equal to the threshold, the sequential `upsweepSequential`
-has to be invoked, and you should return a `Leaf`.
-Otherwise, you should process the part of the input array in parallel, and return
-a `Node`. Make sure that the work is evenly distributed between the parallel computations.
-
-The second phase is called *downsweep* -- here, the algorithm uses the tree to
-push the maximum angle in the corresponding *prefix* of the array to the leaves
-of the tree, and outputs the values.
-Implement the methods `downsweep` which processes parts of the tree in parallel,
-and the method `downsweepSequential`, which traverses the parts of the array
-corresponding to leaves of the tree and writes the final angles into the
-`output` array:
-
-```scala
-def downsweep(input: Array[Float], output: Array[Float],
- startingAngle: Float, tree: Tree): Unit
-
-def downsweepSequential(input: Array[Float], output: Array[Float],
- startingAngle: Float, from: Int, until: Int): Unit
-```
-
-Finally, implement `parLineOfSight` using the `upsweep` and `downsweep` methods:
-
-```scala
-def parLineOfSight(input: Array[Float], output: Array[Float],
- threshold: Int): Unit
-```
-
-Now, run the `LineOfSight` application in sbt and observe the relative speedups:
-
-```
-> runMain reductions.LineOfSightRunner
-```
-
-How large is the speedup compared to the number of cores in your processor?
-Can you explain your results?
diff --git a/labs/lab2-reductions-and-prefix-sums/angle-example-2.svg b/labs/lab2-reductions-and-prefix-sums/angle-example-2.svg
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diff --git a/labs/lab3-k-means/README.md b/labs/lab3-k-means/README.md
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-# K-Means
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b kmeans git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-kmeans
-```
-
-## Useful links
-
- * [A guide to the Scala parallel collections](https://docs.scala-lang.org/overviews/parallel-collections/overview.html)
- * [The API documentation of the Scala parallel collections](https://www.javadoc.io/doc/org.scala-lang.modules/scala-parallel-collections_2.13/latest/scala/collection/index.html)
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-In this assignment, you will implement the K-means algorithm for cluster
-detection, which is used to partition *n* vectors into *k* clusters.
-Here, vectors are separated into clusters based on their mutual similarity --
-vectors that are closer to each other in space are more likely to end up in the
-same cluster, and the distant vectors are likely to be in different clusters.
-K-means has many applications: it is used in data mining, image filtering and
-signal processing.
-
-Here is a simple example -- let's say that we have a set of vectors in 2D space,
-as shown in the following figure:
-
-
-
-As a human, you can visually distinguish the three clusters of points in the
-image:
-
-
-
-When the number of clusters, dimensions and vectors grows, it becomes difficult
-and even impossible to manually determine the clusters.
-K-means is a simple algorithm that takes a set of vectors (called *points*) and
-outputs as set of clusters as follows:
-
-1. Pick `k` points called *means*. This is called *initialization*.
-2. Associate each input point with the *mean* that is closest to it.
- We obtain `k` *clusters* of points, and we refer to this process as
- *classifying* the points.
-3. Update each mean to have the average value of the corresponding cluster.
-4. If the `k` means have significantly changed, go back to step 2.
- If they did not, we say that the algorithm *converged*.
-5. The `k` means represent different clusters -- every point is in the cluster
- corresponding to the closest mean.
-
-Above, two steps need additional discussion.
-First, how do we pick the initial `k` means?
-The initialization step can be done in many different ways -- we will just
-randomly pick some of the input vectors.
-Second, how do we know that the algorithm converged?
-We will check that, for each mean, the square distance between the old value of
-the mean and the new value of the mean is less than or equal to some value
-`eta`.
-
-For a better illustration, here are a few steps of the K-means algorithm.
-Initially, we pick a random set of means, shown with "X" in the figure:
-
-
-
-Then, we classify the points according to the closest mean ("X").
-The means divide the space into regions, where each point is closer to the
-corresponding mean than any other mean -- in the figure, the dotted line depicts
-the borders of different regions:
-
-
-
-All the points in the same region form one cluster. After having classified the
-points, we can update the mean values to the average of all the points in the
-cluster:
-
-
-
-Each of the means was significantly updated.
-This is a good indication that the algorithm did not yet converge,
-so we repeat the steps again -- we first classify all the points:
-
-
-
-And then we update the means again:
-
-
-
-One of the means did not change at all in the last step.
-Still, other means have changed so we continue this process until the change
-in the position of each point drops below the `eta` value.
-
-At each iteration of K-means, we can associate multiple points to clusters,
-and compute the average of the `k` clusters, in parallel.
-Note that the association of a point to its cluster is independent of the
-other points in the input, and similarly, the computation of the average of a cluster
-is independent of the other clusters.
-Once all parallel tasks of the current iteration complete,
-the algorithm can proceed to the next iteration.
-
-K-means is an example of a *bulk synchronous parallel* algorithm (BSP).
-BSP algorithms are composed from a sequence of supersteps, each of which
-contains:
-
-- *parallel computation*, in which processes independently perform local
- computations and produce some values
-- *communication*, in which processes exchange data
-- *barrier synchronisation*, during which processes wait until every process
- finishes
-
-Data-parallel programming models are typically a good fit for BSP algorithms,
-as each bulk synchronous phase can correspond to some number of data-parallel
-operations.
-
-
-## Classifying the points
-
-In the first part of this assignment, you will classify the input points
-according to the square distance to the means.
-Input points are described with the following `Point` data-type:
-
- class Point(val x: Double, val y: Double, val z: Double)
-
-You will start by implementing the `classify` method:
-
- def classify(points: ParSeq[Point], means: ParSeq[Point]): ParMap[Point, ParSeq[Point]]
-
-This method take a sequence of points and a sequence of means, and return
-a map collection, which maps each mean to the sequence of points in the corresponding
-cluster.
-
-Hint: Use `groupBy` and the `findClosest` method, which is already defined for
-you. After that, make sure that all the means are in the resulting map, even if their
-sequences are empty.
-
-
-## Updating the means
-
-In the second part of this assignment, you will update the means corresponding
-to different clusters.
-
-Implement the method `update`, which takes the map of classified points produced
-in the previous step, and the sequence of previous means.
-The method returns the new sequence of means:
-
- def update(classified: ParMap[Point, ParSeq[Point]], oldMeans: ParSeq[Point]): ParSeq[Point]
-
-Take care to preserve order in the resulting sequence -- the mean `i` in
-the resulting sequence must correspond to the mean `i` from `oldMeans`.
-
-Hint: Make sure you use the `findAverage` method that is predefined for you.
-
-
-## Detecting convergence
-
-Finally, you will implement convergence detection.
-The convergence detection method takes a sequence of old
-means and the sequence of updated means, and returns a boolean indicating if the
-algorithm converged or not.
-Given an `eta` parameter, `oldMeans` and `newMeans`, it returns `true` if the
-algorithm converged, and `false` otherwise:
-
- def converged(eta: Double, oldMeans: ParSeq[Point], newMeans: ParSeq[Point])
-
-The algorithm converged iff the square distance between the old and the new mean is less
-than or equal to `eta`, for all means.
-
-Note: the means in the two lists are ordered -- the mean at `i` in `oldMeans`
-is the previous value of the mean at `i` in `newMeans`.
-
-Implement `converged`!
-
-
-## Running the algorithm
-
-We now have everything we need to run the K-means algorithm.
-We only need to combine the previously defined methods in the right way.
-
-The tail-recursive `kMeans` method takes a sequence of
-points `points`, previously computed sequence of means `means`, and the `eta`
-value:
-
- @tailrec final def kMeans(points: ParSeq[Point],
- means: ParSeq[Point], eta: Double): ParSeq[Point]
-
-The `kMeans` method should return the sequence of means,
-each corresponding to a specific cluster.
-
-Hint: `kMeans` implements the steps 2-4 from the K-means pseudocode.
-
-You can use the following command from within `sbt` to benchmark your code, the
-parallel time shoul be significantly faster than the sequential time if your
-computer has more than one core:
-
- > runMain kmeans.KMeansRunner
-
-
-## Use cases
-
-And now for the fun part -- the K-means algorithm has a lot of use-cases!
-
-In image processing applications, it can be used to reduce the size of the
-color palette, thus compressing the image. This is done by turning a
-[true color image](http://en.wikipedia.org/wiki/Color_depth), where each pixel
-is encoded into 32 bits, into [indexed color](http://en.wikipedia.org/wiki/Indexed_color),
-where each pixel can be encoded with just a few bits. This is done by using k-means to
-"cluster" the important colors in the image, thus reducing its palette from
-24-bit (`2^24` colors) to just 32 indexed colors, chosen from the 24-bit palette.
-Here, pixels from the image are the input vectors,
-and their coordinates are the different color channels.
-
-This is the original true color (24-bit) image:
-
-
-
-And this is the indexed color (32 colors) version of it:
-
-
-
-So, thanks to your k-means implementation, ScalaShop can now compress images!
-You can start ScalaShop by invoking from `sbt`:
-
- > runMain kmeans.fun.ScalaShop
-
-The k-means algorithm is very sensitive to the initial choice of means. There
-are three choice strategies implemented in ScalaShop:
-
-* `Uniform Choice` is the simplest strategy. It chooses `n` colors uniformly in
-the entire color space, regardless of the colors used in the image. If the image
-has a dominant color, the means created by this strategy will likely be very far
-away from the clusters formed by this dominant color. You can try setting the
-`Uniform Choice` strategy with 1, 10 and 30 steps. You will notice the initial
-choice is quite bad, but the quality improves as the k-means algorithm is applied
-in more steps.
-* `Random Sampling` is another simple strategy, but with better results. For the
-initial means, it randomly samples `n` colors from the image. This yields good
-results if the image has few dominant colors, but it cannot handle subtle nuances
-in the image. Again, if you try this strategy with 1, 10 and 30 k-means iteration
-steps, you will notice improvements as the k-means algorithm is ran more.
-* `Uniform Random` is the most complex strategy to pick means, but it also produces
-the best results. It works by uniformly splitting the color space in sub-spaces.
-It then counts the number of pixels that have colors belonging to that sub-space.
-Based on this number, it chooses a proportional number of means in the sub-space,
-by randomly sampling from the pixels in that sub-space. Therefore, if your image
-has dominant colors, this strategy will drop a proportional number of means for
-each dominant color, thus allowing the k-means algorithm to capture fine nuances.
-
-In the EPFL image now available in ScalaShop, the mountains are a good way to see
-how well each initial choice of means fares. You also have different strategies
-for deciding convergence:
-
-* `Steps` allows to run a fixed number of steps. After this, the k-means algorithm
-is stopped.
-* `Eta` corresponds to the means stability, as we showed earlier: if the
-means did not move much since the last iteration, the result is considered stable.
-* `Sound-to-noise` ratio is a more refined convergence strategy, which does not settle
-for stability but tries to minimize the difference between the true color image
-and the index color one. This strategy goes beyond `Eta`, but high Sound-to-noise
-ratios will prevent the k-means algorithm from finishing!
-
-With this in mind, enjoy ScalaShop, the ultimate image manipulation tool and
-the nice, warm, sunny photo of EPFL!
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-# Barnes-Hut Simulation
-
-Use the following commands to make a fresh clone of your repository:
-
-```sh
-git clone -b barneshut git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-barneshut
-```
-
-## Useful links
-
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-In this assignment, you will implement the parallel Barnes-Hut algorithm for *N-body simulation*.
-N-body simulation is a simulation of a system of *N* particles that interact with physical
-forces, such as gravity or electrostatic force.
-Given initial positions and velocities of all the *particles* (or *bodies*), the N-body simulation
-computes the new positions and velocities of the particles as the time progresses.
-It does so by dividing time into discrete short intervals, and computing the
-positions of the particles after each interval.
-
-<!--
-Let us recall some basics of classical mechanics.
-What makes a particle, such as an atom, a comet or a star, move through space?
-First, the particle moves through space as a consequence of having some initial velocity
-compared to other particles.
-Second, the particle changes its velocity as a result of external forces.
-These forces are in direct correlation with the proximity of other particles.
-For example, Earth moves through space with some initial velocity,
-which is constantly being changed due to the gravitational force between the Earth and the Sun.
--->
-
-Before we study the Barnes-Hut algorithm for the N-body simulation problem,
-we will focus on a simpler algorithm -- the *direct sum N-body algorithm*.
-The direct sum algorithm consists of multiple iterations, each of which performs
-the following steps for each particle:
-
-1. The particle position is updated according to its current velocity (`delta` is a short time period).
-
- x' = x + v_x * delta
- y' = y + v_y * delta
-
-2. The net force on the particle is computed by adding the individual forces from all the other particles.
-
- F_x = F_1x + F_2x + F_3x + ... + F_Nx
- F_y = F_1y + F_2y + F_3y + ... + F_Ny
-
-3. The particle velocity is updated according to the net force on that particle.
-
- v_x' = v_x + F_x / mass * delta
- v_y' = v_y + F_y / mass * delta
-
-In this exercise, we will assume that the force between particles is the *gravitational force*
-from classical mechanics. Let's recall the formula for the gravitational force between two stellar bodies:
-
- F = G * (m1 * m2) / distance^2
-
-Above, `F` is the absolute value of the gravitational force, `m1` and `m2` are the masses of the two bodies,
-and `r` is the distance between them. `G` is the gravitational constant.
-
-<!--
-The gravitational force vector always points towards the other body.
-The `F_x` and `F_y` components of the gravitational force on the body `m1`
-can be computed by observing the following triangle similarity:
-
-```scala
-distance = math.sqrt((x2 - x1) ^ 2 + (y2 - y1) ^ 2)
-F_x / F = (x2 - x1) / distance
-F_y / F = (y2 - y1) / distance
-```
-
-This is shown in the following figure:
-
-
--->
-
-For each particle, the net force is computed by summing the components of individual forces from all other particles,
-as shown in the following figure:
-
-
-
-The direct sum N-body algorithm is very simple, but also inefficient.
-Since we need to update `N` particles, and compute `N - 1` force contributions for each of those particles,
-the overall complexity of an iteration step of this algorithm is `O(N^2)`.
-As the number of particles grows larger, the direct sum N-body algorithm becomes prohibitively expensive.
-
-The Barnes-Hut algorithm is an optimization of the direct sum N-body algorithm,
-and is based on the following observation:
-
-> If a cluster of bodies is sufficiently distant from a body *A*, the net force on *A*
-> from that cluster can be approximated with one big body with the mass of all the
-> bodies in the cluster, positioned at the center of mass of the cluster.
-
-This is illustrated in the following figure:
-
-
-
-To take advantage of this observation, the Barnes-Hut algorithm relies on
-a *quadtree* -- a data structure that divides the space into cells, and answers queries
-such as 'What is the total mass and the center of mass of all the particles in this cell?'.
-The following figure shows an example of a quadtree for 6 bodies:
-
-
-
-Above, the total force from the bodies *B*, *C*, *D* and *E* on the body *A* can be approximated
-by a single body with *mass* equal to the sum of masses *B*, *C*, *D* and *E*,
-positioned at the center of mass of bodies *B*, *C*, *D* and *E*.
-The center of mass `(massX, massY)` is computed as follows:
-
-```scala
-mass = m_B + m_C + m_D + m_E
-massX = (m_B * x_B + m_C * x_C + m_D * x_D + m_E * x_E) / mass
-massY = (m_B * y_B + m_C * y_C + m_D * y_D + m_E * y_E) / mass
-```
-An iteration of the Barnes-Hut algorithm is composed of the following steps:
-
-1. Construct the quadtree for the current arrangement of the bodies.
- 1. Determine the *boundaries*, i.e. the square into which all bodies fit.
- 2. Construct a quadtree that covers the boundaries and contains all the bodies.
-2. Update the bodies -- for each body:
- 1. Update the body position according to its current velocity.
- 2. Using the quadtree, compute the net force on the body by adding the individual forces from all the other bodies.
- 3. Update the velocity according to the net force on that body.
-
-It turns out that, for most spatial distribution of bodies,
-the expected number of cells that contribute to the net force on a body is `log n`,
-so the overall complexity of the Barnes-Hut algorithm is `O(n log n)`.
-
-Now that we covered all the necessary theory, let's finally dig into the implementation!
-You will implement:
-
-- a quadtree and its combiner data structure
-- an operation that computes the total force on a body using the quadtree
-- a simulation step of the Barnes-Hut algorithm
-
-Since this assignment consists of multiple components,
-we will follow the principles of *test-driven development* and test each component separately,
-before moving on to the next component.
-That way, if anything goes wrong, we will more precisely know where the error is.
-It is always better to detect errors sooner, rather than later.
-
-
-## Data Structures
-
-We will start by implementing the necessary data structures: the quadtree,
-the body data-type and the sector matrix.
-You will find the stubs in the `package.scala` file of the `barneshut` package.
-
-
-### Quadtree Data Structure
-
-In this part of the assignment, we implement the quadtree data structure,
-denoted with the abstract data-type `Quad`.
-Every `Quad` represents a square cell of space, and can be one of the following node types:
-
-- an `Empty` node, which represents an empty quadtree
-- a `Leaf` node, which represents one or more bodies
-- a `Fork` node, which divides a spatial cell into four quadrants
-
-The definition of `Quad` is as follows:
-
-```scala
-sealed abstract class Quad {
- def massX: Float
- def massY: Float
- def mass: Float
- def centerX: Float
- def centerY: Float
- def size: Float
- def total: Int
- def insert(b: Body): Quad
-}
-```
-
-Here, `massX` and `massY` represent the center of mass of the bodies in the respective cell,
-`mass` is the total mass of bodies in that cell, `centerX` and `centerY` are the coordinates
-of the center of the cell, `size` is the length of the side of the cell,
-and `total` is the total number of bodies in the cell.
-
-Note that we consider the top left corner to be at coordinate (0, 0). We also consider the x axis to grow to the right and the y axis to the bottom.
-
-
-
-The method `insert` creates a new quadtree which additionally contains the body `b`,
-and covers the same area in space as the original quadtree.
-Quadtree is an *immutable* data structure -- `insert` does not modify the existing `Quad` object.
-Note that `Body` has the following signature:
-
-```scala
-class Body(val mass: Float, val x: Float, val y: Float, val xspeed: Float, val yspeed: Float)
-```
-
-In this part of the exercise, you only need to know about body's position `x` and `y`.
-
-Let's start by implementing the simplest `Quad` type -- the empty quadtree:
-
-```scala
-case class Empty(centerX: Float, centerY: Float, size: Float) extends Quad
-```
-
-The center and the size of the `Empty` quadtree are specified in its constructor.
-The `Empty` tree does not contain any bodies, so we specify that its center of mass is equal to its center.
-
-Next, let's implement the `Fork` quadtree:
-
-```scala
-case class Fork(nw: Quad, ne: Quad, sw: Quad, se: Quad) extends Quad
-```
-
-This node is specified by four child quadtrees `nw`, `ne`, `sw` and `se`,
-in the northwest, northeast, southwest and southeast quadrant, respectively.
-
-The northwest is located on the top left, northeast on the top right, southwest on the bottom left and southeast on the bottom right.
-
-The constructor assumes that the children nodes that represent four adjacent cells of the
-same size and adjacent to each other, as in the earlier figure.
-The center of the `Fork` quadtree is then specified by, say, the
-lower right corner of the quadtree `nw`. If the `Fork` quadtree is empty, the
-center of mass coincides with the center.
-
-Inserting into a `Fork` is recursive -- it updates the respective child and creates a new `Fork`.
-
-Finally, the `Leaf` quadtree represents one or more bodies:
-
-```scala
-case class Leaf(centerX: Float, centerY: Float, size: Float, bodies: Seq[Body])
-extends Quad
-```
-
-If the `size` of a `Leaf` is greater than a predefined constant `minimumSize`,
-inserting an additonal body into that `Leaf` quadtree creates a `Fork` quadtree
-with empty children, and adds all the bodies into that `Fork` (including the new body).
-Otherwise, inserting creates another `Leaf` with all the existing bodies and the new one.
-
-
-### The Body Data-Type
-
-Next, we can implement the `Body` data-type:
-
-```scala
-class Body(val mass: Float, val x: Float, val y: Float, val xspeed: Float, val yspeed: Float) {
- def updated(quad: Quad): Body = ???
-}
-```
-
-Here, `xspeed` and `yspeed` represent the velocity of the body, `mass` is its mass,
-and `x` and `y` are the coordinates of the body.
-
-The most interesting method on the `Body` is `updated` -- it takes a quadtree and
-returns the updated version of the `Body`:
-
-```scala
-def updated(quad: Quad): Body
-```
-
-This method is already half-completed for you -- you only need to implement
-its nested method `traverse`, which goes through the quadtree and proceeds casewise:
-
-- empty quadtree does not affect the net force
-- each body in a leaf quadtree adds some net force
-- a fork quadtree that is sufficiently far away acts as a single point of mass
-- a fork quadtree that is not sufficiently far away must be recursively traversed
-
-When are we allowed to approximate a cluster of bodies with a single point?
-The heuristic that is used is that the size of the cell divided by the distance
-`dist` between the center of mass and the particle is less than some constant `theta`:
-
-```scala
-quad.size / dist < theta
-```
-
-Hint: make sure you use the `distance` to compute distance between points,
-the `theta` value for the condition, and `addForce` to add force contributions!
-
-Before proceeding, make sure to run tests against your `Quad` and `Body` implementations.
-
-
-### The Sector Matrix
-
-The last data structure that we will implement is the *sector matrix*.
-In this data structure, we will use the auxiliary class `Boundaries`, which
-contains the `minX`, `maxX`, `minY` and `maxY` fields for the boundaries of the scene:
-
-```scala
-class Boundaries {
- var minX: Float
- var minY: Float
- var maxX: Float
- var maxY: Float
- def size = math.max(maxX - minX, maxY - minY)
-}
-```
-
-We will also rely on the `ConcBuffer` data structure, mentioned in the lecture:
-
-```scala
-class ConcBuffer[T]
-```
-
-The `ConcBuffer` class comes with efficient `+=`, `combine` and `foreach` operations,
-which add elements into the buffer, combine two buffers and traverse the buffer, respectively.
-The sector matrix additionally has the `toQuad` method, which returns a quadtree that contains all
-the elements previously added with the `+=` method.
-Recall from the lectures that this combination of methods make the `ConcBuffer` a *combiner*.
-
-The `SectorMatrix` is just a square matrix that covers a square region of space
-specified by the boundaries:
-
-```scala
-class SectorMatrix(val boundaries: Boundaries, val sectorPrecision: Int) {
- val sectorSize = boundaries.size / sectorPrecision
- val matrix = new Array[ConcBuffer[Body]](sectorPrecision * sectorPrecision)
- for (i <- 0 until matrix.length) matrix(i) = new ConcBuffer
- def apply(x: Int, y: Int) = matrix(y * sectorPrecision + x)
-}
-```
-
-The `sectorPrecision` argument denotes the width and height of the matrix, and
-each entry contains a `ConcBuffer[Body]` object. Effectively, the `SectorMatrix` is a *combiner* --
-it partitions the square region of space into `sectorPrecision` times `sectorPrecision` buckets, called *sectors*.
-
-
-
-Combiners such as the `SectorMatrix` are used in parallel programming to partition the results into
-some intermediate form that is more amenable to parallelization.
-Recall from the lecture that one of the ways to implement a *combiner* is by using
-a bucket data structure -- we will do exactly that in this part of the exercise!
-We will add three methods on the `SectorMatrix` that will make it a combiner.
-We start with the `+=` method:
-
-```scala
-def +=(b: Body): SectorMatrix = {
- ???
- this
-}
-```
-
-This method should use the body position, `boundaries` and `sectorPrecision`
-to determine the sector into which the body should go into,
-and add the body into the corresponding `ConcBuffer` object.
-
-Importantly, if the `Body` lies outside of the `Boundaries`, it should be considered
-to be located at the closest point within the `Boundaries` for the purpose of finding which `ConcBuffer` should hold the body.
-
-Next, we implement the `combine` method, which takes another `SectorMatrix`,
-and creates a `SectorMatrix` which contains the elements of both input
-`SectorMatrix` data structures:
-
-```scala
-def combine(that: SectorMatrix): SectorMatrix
-```
-
-This method calls `combine` on the pair of `ConcBuffer`s in `this` and `that`
-matrices to produce the `ConcBuffer` for the resulting matrix.
-You can safely assume that combine will only be called on matrices of same dimensions, boundaries and sector precision.
-
-
-
-
-The nice thing about the sector matrix is that a quadtree can be constructed
-in parallel for each sector. Those little quadtrees can then be linked together.
-The `toQuad` method on the `SectorMatrix` does this:
-
-```scala
-def toQuad(parallelism: Int): Quad
-```
-
-This method is already implemented -- you can examine
-it if you would like to know how it works.
-
-<!--
-Finally, we will implement the `toQuad` method, which uses the `SectorMatrix`
-to create a `Quad` tree in parallel:
-
-```scala
-def toQuad(parallelism: Int): Quad = {
- def BALANCING_FACTOR = 4
- def quad(x: Int, y: Int, span: Int, achievedParallelism: Int): Quad = {
- if (span == 1) {
- ???
- } else {
- ???
- }
- }
- quad(0, 0, sectorPrecision, 1)
-}
-```
-
-The `toQuad` method takes the desired degree of `parallelism` as an argument,
-and then calls the recursive `quad` method.
-The `quad` method operates on a subsquare of the matrix starting at `x`, `y` coordinates,
-with the length `span`.
-The fourth parameter, `achievedParallelism`, describes how many tasks have been
-created at a given call depth.
-
-Implement the `quad` method so that:
-
-1. If `span` is 1, the method sequentially converts the bodies in that sector to a quadtree.
- Use `foldLeft` and the `insert` method on the bodies in each sector to get the quadtree.
-2. Otherwise, if `parallelism > 1 && achievedParallelism < parallelism * BALANCING_FACTOR`,
- the method recursively, and in parallel, creates quadtrees for the `4` subsquares
- and links them together into a bigger quadtree.
-3. Otherwise, if the `parallelism == 1 || achievedParallelism >= parallelism * BALANCING_FACTOR`,
- the method does the same as in case 2, but without parallelism.
-
-Don't forget to multiply `achievedParallelism` by `4` at each step.
--->
-
-Congratulations, you just implemented your first combiner!
-Before proceeding, make sure to run those unit tests.
-
-
-## Implementing Barnes-Hut
-
-Now that we have all the right data structures ready and polished,
-implementing Barnes-Hut becomes a piece of cake.
-
-Take a look at the file `Simulator.scala`, which contains the implementation
-of the Barnes-Hut simulator, and in particular the `step` method.
-The `step` method represents one step in the simulation:
-
-```scala
-def step(bodies: Seq[Body]): (Seq[Body], Quad) = {
- // 1. compute boundaries
- val boundaries = computeBoundaries(bodies)
-
- // 2. compute sector matrix
- val sectorMatrix = computeSectorMatrix(bodies, boundaries)
-
- // 3. compute quadtree
- val quad = computeQuad(sectorMatrix)
-
- // 4. eliminate outliers
- val filteredBodies = eliminateOutliers(bodies, sectorMatrix, quad)
-
- // 5. update body velocities and positions
- val newBodies = updateBodies(filteredBodies, quad)
-
- (newBodies, quad)
-}
-```
-
-The pre-existing code in `step` nicely summarizes what this method does.
-
-
-### Computing the Scene Boundaries
-
-First, we must compute the boundaries of all the bodies in the scene.
-Since bodies move and the boundaries dynamically change,
-we must do this in every iteration of the algorithm.
-The `computeBoundaries` method is already implemented -- it uses the `aggregate`
-combinator on the sequence of bodies to compute the boundaries:
-
-```scala
-def computeBoundaries(bodies: Seq[Body]): Boundaries = {
- val parBodies = bodies.par
- parBodies.tasksupport = taskSupport
- parBodies.aggregate(new Boundaries)(updateBoundaries, mergeBoundaries)
-}
-```
-
-How does this work? The `aggregate` method divides the input sequence into a
-number of chunks. For each of the chunks, it uses the `new Boundaries` expression
-to create the accumulation value, and then folds the values in that chunk
-calling `updateBoundaries` on each body, in the same way a `foldLeft` operation would.
-Finally, `aggregate` combines the results of different chunks using a reduction tree and `mergeBoundaries`.
-
-So, we need the `updateBoundaries` method:
-
-```scala
-def updateBoundaries(boundaries: Boundaries, body: Body): Boundaries
-```
-
-Given an existing `boundaries` object and a body, the `updateBoundaries` updates
-the `minX`, `minY`, `maxX` and `maxY` values so that the boundaries include the body.
-
-Next, the `mergeBoundaries` method creates a new `Boundaries` object, which represents
-the smallest rectangle that contains both the input boundaries:
-
-```scala
-def mergeBoundaries(a: Boundaries, b: Boundaries): Boundaries
-```
-
-Question: Is `mergeBoundaries` associative? Is it commutative? Does it need to be commutative?
-
-Implement these two methods, and test that they work correctly!
-
-
-### Building the Quadtree
-
-Next, we need to build a `Quad` tree from the sequence of bodies.
-We will first implement the `computeSectorMatrix` method to get the `SectorMatrix`:
-
-```scala
-def computeSectorMatrix(bodies: Seq[Body], boundaries: Boundaries): SectorMatrix
-```
-
-Hint: aggregate the `SectorMatrix` from the sequence of bodies, the same way it was used for boundaries.
-Use the SECTOR_PRECISION constant when creating a new `SectorMatrix`.
-
-<!--
-Next, implement `computeQuad`, which converts `SectorMatrix` to a `Quad`:
-
- def computeQuad(sectorMatrix: SectorMatrix): Quad
-
-Hint: use `taskSupport.parallelismLevel` to determine the desired parallelism.
--->
-
-Test that these methods work correctly before proceeding!
-
-
-### Eliminating Outliers
-
-During the execution of the Barnes-Hut algorithm, some of the bodies tend to
-move far away from most of the other bodies. There are many ways to deal with such *outliers*,
-but to keep things simple, we will eliminate bodies that move too fast and too far away.
-
-We will not go into details of how this works, but if you'd like to know more,
-you can try to understand how the `eliminateOutliers` method works.
-
-
-### Updating Bodies
-
-The `updateBodies` method uses the quadtree to map each body from the
-previous iteration of the algorithm to a new iteration:
-
-```scala
-def updateBodies(bodies: Seq[Body], quad: Quad): Seq[Body]
-```
-
-Recall that we already implemented the `updated` method which updates a single body.
-
-
-## Running Barnes-Hut
-
-At last, the parallel Barnes-Hut algorithm is implemented.
-Note that, despite all the work, we kept our Barnes-Hut algorithm implementation simple
-and avoided the details that a more realistic implementation must address. In particular:
-
-- we represented each body as a single point in space
-- we restricted the simulation to two-dimensional space
-- we ignored close encounter effects, such as body collision or tearing
-- we ignored any relativistic effects, and assume classical mechanics
-- we ignored errors induced by floating point computations
-
-You can now run it as follows:
-
- > runMain barneshut.BarnesHut
-
-To visualize the quadtree, press the *Show quad* button, and then hit the *Start/Pause* button.
-
-Play with the parallelism level and the number of bodies, and observe the average speedups in the lower right corner.
-Then sit back, and enjoy the show!
-
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diff --git a/labs/lab5-bounded-buffer/README.md b/labs/lab5-bounded-buffer/README.md
deleted file mode 100644
index ee021347af711f9317b08d7d1e8157c9bb1d6467..0000000000000000000000000000000000000000
--- a/labs/lab5-bounded-buffer/README.md
+++ /dev/null
@@ -1,266 +0,0 @@
-# Concurrent Bounded Buffer
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b pubsub git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-pubsub
-```
-
-## Useful links
-
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-The goal of this assignment is to implement a fixed size buffer that can be accessed concurrently by multiple threads running in parallel.
-This is a simple yet important data structure that is widely used in concurrent applications.
-In particular, we provide you an implementation of a Publish/Subscribe (PubSub) application that uses the bounded buffer you would be developing.
-We will provide a brief overview of the Publish/Subscribe application, and instructions on how to run it towards the end of this assignment.
-However, the only data structure that you should implement for this assignment is the bounded buffer, which is explained below:
-
-## Bounded Buffer - Definition and Operations
-
-A concurrent bounded buffer is a data structure that supports the following two operations:
-- a method `put(e)` that puts a value `e` in the buffer,
-- a method `take` that takes a value from the buffer, and removes it.
-
-Importantly, the `put` and `take` methods should follow the FIFO (First-In, First-Out) semantics.
-Intuitively, this means that `take` should return the _oldest_ element that was `put` into the buffer.
-<!-- [comment]: # (A pointer to linearizability ?) -->
-
-The buffer is *bounded*, which means that it can only store a finite number of elements.
-The maximum number of elements the buffer can hold is passed as an argument to the constructor of `BoundedBuffer`.
-The following is the definition of the data structure, which can be found in the file:
-`src/main/scala/pubsub/collection/BoundedBuffer`
-
-```scala
-class BoundedBuffer[T](size: Int) extends AbstractBoundedBuffer[T](size) {
- override def put(e: T): Unit = ???
- override def take(): T = ???
-}
-```
-
-A `put` operation invoked on a buffer that is full will *wait* (or block) until an element is taken from
-the buffer. Dually, a `take` operation invoked on an empty buffer will *wait* until an element is put
-into the buffer.
-Note that multiple `put` and `take` operations can be initiated concurrently from different threads. Hence,
-care must be taken to ensure that the `buffer` is not accessed or left in an inconsistent state by the `put` or `take`
-methods. To understand the challenges better let us start by considering a sequential setting.
-
-## A Sequential Bounded Buffer
-
-Let us try to implement the `BoundedBuffer` class without worrying at all about concurrency.
-That is, imagine that there is only one thread that performs a sequence of `put` and `take` operations on an object of type `BoundedBuffer`.
-Can you implement the `put` and `take` methods in this setting?
-Note that your implementation should run in constant time in the worst-case.
-(A remark: the data structure you implement in this case is also called a _circular queue_.)
-
-To write your solution, use the `pubsub.collection.BoundedBuffer` class skeleton that is given to you in the handout.
-You are given two _variables_: `count` and `head`, and an internal buffer that exposes a minimal array-like API.
-It is important that you use the variables given to you, and the internal buffer API.
-*You are strongly advised not to create any other fields or classes or objects*. Otherwise,
-you may fail the testcases.
-
-The following is an illustration of the input/output behavior of the buffer in the sequential case.
-In the comments, we show the contents of the array used internally by the `BoundedBuffer` class, and the
-return values (if any) of the operations.
-
-```scala
-def example1() = { // Internal Buffer:
- val buf = new BoundedBuffer(2) // | | |
- buf.put(1) // | 1 | |
- buf.put(2) // | 1 | 2 |
- val x = buf.take() // | | 2 | and x is 1
- buf.put(3) // | 3 | 2 |
- val y = buf.take() // | 3 | | and y is 2
-}
-```
-
-Now, think about what should happen if we try to `put` an element into a buffer that is full?
-Obviously, the element cannot be stored in the buffer. Furthermore, there is no use of waiting for
-some space to be created in the buffer because no element in the buffer could be read simultaneously.
-(Recall that there is only one thread accessing the buffer in the sequential setting).
-Hence, you may throw an exception in this case. Similarly, handle the dual case of invoking a
-`take` on an empty buffer.
-
-Think about what happens when your sequential buffer is used in a concurrent system. What can happen?
-Try running the test suite that was given to you with the handout. What can you see?
-
-As you may have seen, the test suite was able to find many problematic schedules for the non-concurrent buffer. This is to be expected!
-Now, using your sequential implementation as a stepping stone, let us develop a concurrent bounded buffer, which is your real goal.
-
-## Concurrent Bounded Buffer
-
-Now, let us imagine that there are two threads currently performing operations on a shared buffer of
-max size 2 as shown below:
-
-```scala
-val buf = new BoundedBuffer(2)
- Thread 1 Thread 2
- buf.put(1) y = buf.take()
-```
-
-The first change you need to make to your sequential implementation is that `buf.take()` invoked
-by `Thread 2` should *wait* until an element is put into the buffer by `Thread 1`.
-Similarly, a `put` method should *wait* until an element is taken from the buffer.
-Throwing an exception in these cases as in the sequential case is not acceptable
-here because operations can be happening simultaneously. In other words, a `put` that cannot
-succeed now can succeed in the future, because of a concurrently executing `take`.
-
-For this purpose, you may use a `wait` method of the Java standard library.
-You can find a brief description of the semantics of the method, and references for further reading in the section *Synchronization Primitives* below. An alternative is to loop continuously until the buffer has an element. This is called as *busy waiting*. But as you can imagine this wastes precious processor cycles, and hence is not preferred. (See the section on busy waiting as well for some examples.)
-
-With this waiting strategy, in the above example, `Thread 2` will enter into *wait* state if it executes before `Thread 1`,
-and `Thread 1` will proceed to execute. However, after completing the execution `Thead 1` should *wake up*
-`Thread 2` (and also every other thread that would be waiting on the buffer to become non-empty).
-For this purpose, you have to use the `notify()`, and/or `notifyAll()` primitives (see section *Synchronization Primitives*).
-Note that there are subtle differences between `notify()` and `notifyAll()` which is also briefly explained below.
-
-*Important Hint: You should invoke the synchronization primitives like `wait`, `notify`, `synchronized` etc. only
-on the `this` reference when implementing the methods of the `BoundedBuffer` class. Otherwise, your
-implementation may get stuck (and run into deadlocks) while testing*
-
-Also remember that `notify` and `wait` should be invoked only inside a `synchronized` block.
-
-Now that we know how to implement the waiting strategy to alleviate the above problem of `Thread 2`
-running before `Thread 1`, let us look at other issues that may arise when `put` and `take` methods run concurrently.
-In particular, say `Thread 1` starts before `Thread 2`.
-Let us study how the operations they perform can interleave, assuming that you implemented
-them using `count` and `head`.
-Consider the following interleaving of operations of `put` and `get`
-
-```scala
-val buf = new BoundedBuffer(2)
- Thread 1 Thread 2
- //start buf.put(1)
- //start buf.take
- //reads count -> 0
- //...
- //updates count -> 1
- //reads count -> 1
- //read internal buffer(0) (crash !!)
- //internal buffer(0) <- 1
-```
-
-Clearly, if your code permits the above interleaving then it would crash. Now, your goal is to come up
-with adequate synchronization so that no interleaving of operations of `put` and `take` performed
-by any number of threads result either in a crash or in an incorrect buffer state.
-You may use all the synchronization primitives described below.
-
-The test suite provided along with the project may help you find problematic schedules.
-A schedule is a sequence of operations carried out by the different threads such as the one
-shown above.
-The test suite runs your code on several thousand schedules. You may use that to check whether your bounded
-buffer reaches a consistent state on some schedule.
-
-An incorrect run on a schedule could either be that:
-- An operation returns an incorrect value.
-- An operation throws an exception when it should not.
-
-Remember that some operations, even though they look *atomic*, are decomposed into multiple smaller operations.
-For instance, consider `buffer(head) = ...`. Is this atomic ?
-In the remaining sections, we talk about the synchronization primitives and some of the commons cases of errors.
-
-## Synchronization Primitives
-
-- `synchronized[T](body: => T)`, which executes `body` in a critical section. This ensures that only a single thread at a time can execute the instructions passed as argument.
-- [`wait()`][1], which stops the execution of the thread until it is notified. `wait()` can only be called within a `synchronized` block.
-- [`notify()`][2], which unblocks one of the waiting thread. Note that it's not possible to specify which thread is unblocked.
-- [`notifyAll()`][3], which unblocks all of the waiting threads.
-
-## Using the Pub/Sub Server
-The following section describes how you can run and use the Pub/Sub server implemented for you,
-This small server allows any amount of people to connect and chat together around topics of their choice. Playing with it may help you discover errors in your ConcurrentBoundedBuffer.
-
-### Running the server
-You can start the Pub/Sub Server as any normal SBT project, simply type `sbt run` while in the root folder of the project. No additional setup is required.
-
-### Connecting to the server
-You can connect to the server via command-line interface and the [`telnet`][4] protocole.
-When the server is started it will display a line such as:
-
- Connect to <server_ip> (or `localhost`), port 7676 with `telnet` to join this server
-Which gives you the IP address of the running server.
-In your command-line you must do the following telnet command: `telnet <server_ip> 7676`
-You should then be greeted by the server with a `connection_ack`.
-
-*telnet is not enabled by default on Windows 8 and 10; [here are fast and simple instructions to enable telnet on recent Windows][5]*
-
-*There are also issues with telnet connecting to the server when using Windows 10 due to firewall issues. However on Linux telnet works flawlessy [sorry for any inconvenience]*
-
-### Commands available
-Now that the server is running and you have a connected client, here is the comprehensive list of the commands any client can use on the server:
-
-- `rename <nickname>`: change your display name to `<nickname>`. Your new name cannot contain any space or single quote.
-- `subscribe <topic>`: add yourself to the `<topic>` subscriber list. You will receive all messages sent to that topic.
-- `unsubscribe <topic>`: remove yourself from the `<topic>` subscriber list. You will no longer receive messages sent to that topic.
-- `publish <topic> '<message>'`: publish the `<message>` of your choice to the `<topic>` of your choice. Your `<message>` can span on multiple lines but has to be enclosed with single-quote `'` to be deemed valid.
-- `leave`: end the connection with the server in a polite manner
-
-
-## Common Pitfalls
-
-### Busy Waiting
-
-Consider the following code snippet that uses a while loop (with an empty body) to guard the execution of the `put` and `take` operations.
-
-```scala
-class BoundedBuffer[T](size: Int) extends AbstractBoundedBuffer[T](size) {
- override def put(e: T): Unit = {
- while (isFull) {}
- // Rest of your code
- ???
- }
- override def take(): T = {
- while (isEmpty) {}
- // Rest of your code
- ???
- }
-}
-```
-
-What is the problem with this way of waiting? Apart from those potential problems,
-is this code behaving correctly in a concurrent setting i.e, is there a schedule that would
-result in an incorrect behavior on the above code.
-_Hint: what happens when one `put` and two `take` are running in parallel ?_
-
-### `notify()` vs `notifyAll()`
-
-As per the documentation [documentation][2]:_ of `notify()`, invoking the `notify` method on an object `o` wakes up a _single_ thread that is waiting on the object `o`'s monitor. If there are multiple threads waiting on `o`, one of them is chosen **arbitrarily** at the discretion of the implementation. Note that a thread waits on an object's monitor by calling one of the wait methods.
-
-Using the `notify` method often leads to tricky problems which can sometimes result in deadlocks. This can lead to bugs that can be hard to see and reproduce. For this reason, it is often strongly advised to use `notifyAll` instead.
-Can you figure out why this is the case?
-_Hint: Could it ever happen that the thread unblocked by notify somehow cannot execute even though there are other threads that could execute if they were unblocked instead ?_
-
-### Waiting using `if` vs `while`
-
-Due to the semantics of `notify` and `notifyAll`, there are no guarantees that the condition on which a thread is waiting will be fulfilled when the thread is unblocked. Therefore, the following code is problematic:
-
-```scala
-synchronized {
- if (!condition) {
- wait()
- }
- // We have no garantees that the condition holds here.
-}
-```
-
-Instead, the waiting thread should always recheck the condition when it is unblocked. This can easily be done using a `while` instead of `if`.
-
-Can you come up with a schedule in which using a simple `if` would be problematic?
-
-_Hint: Try using a system with 3 threads, two of which try to execute `take` and one of which executes `put` in an initially empty buffer?_
-
-_Hint: Note that, as specified in the [documentation of `notify`][2]:_
-> The awakened thread will not be able to proceed until the current thread relinquishes the lock on this object. The awakened thread will compete in the usual manner with any other threads that might be actively competing to synchronize on this object; for example, **the awakened thread enjoys no reliable privilege or disadvantage in being the next thread to lock this object**.
-
-[1]: https://docs.oracle.com/en/java/javase/15/docs/api/java.base/java/lang/Object.html#wait()
-[2]: https://docs.oracle.com/en/java/javase/15/docs/api/java.base/java/lang/Object.html#notify()
-[3]: https://docs.oracle.com/en/java/javase/15/docs/api/java.base/java/lang/Object.html#notifyAll()
-[4]: https://en.wikipedia.org/wiki/Telnet
-[5]: http://www.sysprobs.com/install-and-enable-telnet-in-windows-8-use-as-telnet-client
diff --git a/labs/lab6-lock-free-sorted-list/README.md b/labs/lab6-lock-free-sorted-list/README.md
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index 820bb19ffc7cb58768ab7c859e5a3bdcb13a9045..0000000000000000000000000000000000000000
--- a/labs/lab6-lock-free-sorted-list/README.md
+++ /dev/null
@@ -1,193 +0,0 @@
-# Lock-free Sorted List
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b lockfree git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-lockfree
-```
-
-## Useful links
-
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-The goal of this assignment is to implement a concurrent list that stores values of type `Int` in ascending order.
-Last week, we have seen how to use a monitor to make data structures operations behave correctly in a concurrent setting.
-This week, the data structure we will implement will not use a monitor or a lock of any kind.
-Instead, we will use an atomic operation, called `compareAndSet`, in your implementation of the data structure operations.
-
-The goal will still be to have a data structure that can safely be used in a concurrent setting.
-In addition, the sorted list operations you will implement will be *lock-free*.
-In a system where all concurrent operations have this property, the progress of at least one thread is ***guaranteed***.
-There can not be a global deadlock in which none of the thread can make progress!
-Note that this does *not* guarantee that all threads can make progress.
-Operation with this property is said to be *wait-free*.
-
-## Atomic Variables
-
-The list methods you will implement will make use of atomic variables. An atomic variable is an object which holds a value of a given type `T`. The object offers two methods to manipulate this value:
-- `get`, which returns the current value held in the variable,
-- `compareAndSet`, which modifies the content of the variable provided that the previous value is given. This method returns `true` if it was successful, `false` otherwise.
-
-The operation `compareAndSet` applies the following logic ***atomically***, which means there cannot be any context switches between the instructions of the operation:
-```scala
-def compareAndSet(expected: T, value: T) {
- if (current == expected) {
- current = value
- true
- }
- else {
- false
- }
-}
-```
-
-The `AtomicVariable` class is given to you. You do not have to worry about implementing it!
-
-Note that a similar class exists in the Java standard library, called [`AtomicReference`](https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/atomic/AtomicReference.html).
-The main difference between `AtomicReference` and the `AtomicVariable` class we defined for this exercise is that `AtomicReference` uses referential equality to check whether `expected` and `current` are the same, while `AtomicVariable` uses the `==` method.
-
-## The Sorted List Data Structure
-
-The data structure you will implement is a mutable linked list of integers, sorted in ascending order. Each integer is stored in a `Node`. In addition to the integer value, each node contains a mutable reference to the next node in the list. This mutable reference is held in an `AtomicVariable`.
-
-```scala
-abstract class Node(val value: Int, initTail: Option[Node]) {
-
- type State = Option[Node]
-
- def initialState: State = initTail
-
- val atomicState: AbstractAtomicVariable[State] = new AtomicVariable[State](initialState)
-
- def next: Option[Node] = atomicState.get
-
- // ...
-}
-```
-
-Then, `SortedList` is simply a class that holds a reference to the first node of the list. This class is defined in `SortedList.scala`.
-
-```scala
-class SortedList extends AbstractSortedList {
-
- // The sentinel node at the head.
- private val _head = createNode(0, None, isHead=true)
-
- // The first logical node is referenced by the head.
- def firstNode: Option[Node] = _head.next
-
- def findNodeWithPrev(pred: Int => Boolean): (Node, Option[Node]) = ???
-
- def insert(e: Int): Unit = ???
-
- def contains(e: Int): Boolean = ???
-
- def delete(e: Int): Boolean = ???
-}
-```
-
-The value `_head` of the list is called a [*sentinel* node](https://en.wikipedia.org/wiki/Sentinel_node). This value is simply `Node` that will serve only as a reference to the first actual node of the list. The value (viz. zero) held by this special node should be completely ignored. This technique is useful because it limits the duplication of code. An alternative is to have a reference to the first node using an `AtomicVariable`. But this would force us to handle the first node differently from the rest. So will use a sentinel node for this assignment.
-
-### `findNodeWithPrev`
-
-The first method you will implement is an internal helper method which will be used all other methods.
-This method should do a traversal of the list to find the first node whose value satisfies the parameter predicate.
-The method should return the following two values as a pair:
-- the predecessor of the node,
-- the node.
-
-If `node` is the first node whose value satisfies the predicate and `predecessor` its predecessor, then the method should return `(predecessor, Some(node))`. Due to the use of the sentinel node at the head of the list, the method is bound to find a predecessor, even for the first logical node.
-
-When the predicate doesn't hold on any of the values, then the function should return `(last, None)`, where
-`last` is the last node of the list.
-
-### `insert`
-
-Your first goal will be to code the insert method of the lock-free list. The idea is very simple:
-
-1. Locate the position you need to insert by using the `findNodeWithPrev` method you have just implemented.
-2. Create a new node that holds the value and points to the correct next node. For this, you should use `createNode(value, nextNode)`.
-3. Use the `compareAndSet` operation to make the previous node point to your newly created node.
-4. If the operation failed, retry from the start! Otherwise, the operation is done!
-
-### `contains`
-
-Next, you should implement the `contains` method, which checks if the list contains a given element. You may of course use the `findNodeWithPrev` method you have implemented.
-
-### `delete`
-
-With the addition of `delete`, things get rapidly trickier. The intuitive solution would be to:
-
-1. Find the node to delete and its predecessor using `findNodeWithPrev`.
-2. Use an atomic `compareAndSet` operation to make the predecessor node point to the successor.
-
-Unfortunately, this solution is not entirely satisfactory!
-Indeed, there exist schedules in which some successful operations are lost. Can you think of any?
-
-As an example, assume that the list currently contains the values `10` and `30`.
-
-
-- Imagine one thread starts executing the insertion of `20` in the list and correctly locates where to insert the new node (between `10` and `30`). The thread then creates a node with value `20` and makes it point to the node with value `30`. 
-- Now, some other thread is scheduled and starts executing the deletion of the node with value `10`. The thread finds the two nodes that surround it, that is the sentinel node at the head of the list and the node with value `30` (note that the insertion of `20` by the other thread isn't visible yet!). The thread, using compare and set, changes the next pointer of the sentinel node to the node with value `30`. The `delete` operation then terminates successfully. 
-- Now, the first thread resumes its execution. Its next instruction is to use compare and set to change the next pointer of the node with value `10`, which it can do without any problem! Indeed, this atomic variable wasn't touched by the delete operation. The `insert` operation looks successful, even though the node that was inserted is not reachable from the head of the list! The operation was lost... 
-
-One solution to this problem, as proposed by Timothy L. Harris in his paper [A Pragmatic Implementation of Non-Blocking Linked Lists](https://www.cl.cam.ac.uk/research/srg/netos/papers/2001-caslists.pdf), is to somehow record in the node that it was deleted.
-We will proceed similarly in this assignment.
-
-#### Marking Nodes
-
-The first step is to modify the mutable state held by nodes. Open the `Node.scala` file. Until now the state was just `Option[Node]`, which is an optional reference to the next node.
-You will need to add, in addition to this information, a boolean flag that indicates whether the node was deleted or not.
-
-
-```scala
-abstract class Node(val value: Int, initTail: Option[Node]) {
-
- type State = ???
-
- def initialState: State = ???
-
- val atomicState: AbstractAtomicVariable[State] = new AtomicVariable[State](initialState)
-
- def next: Option[Node] = ???
-
- def deleted: Boolean = ???
-
- def mark: Boolean = ???
-}
-```
-
-Begin by modifying the `State` type to any type that you deem appropriate. Remember that the value of this type should contain an `Option[Node]` (the next node) and a flag (whether the node has been marked).
-
-Complete the other functions, described below, as well:
-- `initialState` should return the state of the node when it is first created. Note that the node should not be marked and the optional reference to the next node should be set to `initTail`.
-- `next` should return the optional reference to the next node held currently in the state.
-- `deleted` should return `true` if the node was marked, `false` otherwise.
-- `mark` should mark the node as `deleted`. The method should return `true` only if it is the first time this node is marked. If the node was previously marked, this method should return `false`. (Think carefully about this!)
-
-#### Implementing `delete` of `SortedList`
-
-Now, we can implement a version of delete that is not vulnerable to the problem exposed earlier. The idea is to:
-
-1. Find the node to delete and its predecessor using `findNodeWithPrev`.
-2. Mark the node as `deleted` using its `mark` method. If it returns false, retry from the start!
-
-Once this is done, we still have to modify the other list methods to handle the fact that nodes can be marked.
-Marked nodes will be deleted within the `findNodeWithPrev` method.
-
-#### Adapting `findNodeWithPrev` to handle marked nodes
-
-The first method you need to adapt is `findNodeWithPrev`. The method should do exactly as before except when it encounters a marked node during its traversal. When it does so, the method should use compare and set to modify the next node pointed by the predecessor node. Regardless of the outcome of the compare and set operation, the `findNodeWithPrev` should then restart from scratch.
-
-#### Adapting `insert` to handle marked nodes
-
-The last method you need to change is `insert`. The logic remains exactly the same, except that when you perform the `compareAndSet` operation, you should make sure that the parameters are of type `State` and are not flagged as deleted.
-
-You are now done with this assignment. Congratulations!
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diff --git a/labs/lab7-actors-binary-tree/README.md b/labs/lab7-actors-binary-tree/README.md
deleted file mode 100644
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-## Binary Trees
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b actorbintree git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-actorbintree
-```
-
-## Useful links
-
- * [The API documentation of Akka](https://doc.akka.io/api/akka/current/akka/index.html)
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-Binary trees are tree based data structures where every node has at most two children (left and right).
-In this exercise, every node stores an integer element.
-From this we can build a binary search tree by requiring for every node that
-
- * values of elements in the left subtree are strictly smaller than the node's element
- * values of elements in the right subtree are strictly bigger than the node's element
-
-In addition, there should be no duplicates, hence we obtain a binary tree set.
-
-Your task in this assignment is to implement an actor-based binary tree set where each node is
-represented by one actor. The advantage of such an actor-based solution is that it can execute
-fully asynchronously and in parallel.
-
-
-#### The API
-
-You can find the message-based API for the actor-based binary tree to be implemented in the supplied `BinaryTreeSet` object
-in the file `BinaryTreeSet.scala`.
-
-The operations, represented by actor messages, that the implementation should support are the following:
-
- * `Insert`
- * `Remove`
- * `Contains`
-
-All three of the operations expect an `ActorRef` representing the requester of the operation, a numerical identifier of
-the operation and the element itself. `Insert` and `Remove` operations should result in an `OperationFinished` message sent
-to the provided requester `ActorRef` reference including the id of the operation. `Insert` and `Remove` should return an
-`OperationFinished` message even if the element was already present in the tree or was not found, respectively.
-`Contains` should result in a `ContainsResult` message containing the result of the lookup (a Boolean which is
-true if and only if the element is in the tree when the query arrives) and the identifier of the `Contains` query.
-
-#### Handling of Removal
-
-You should observe that both the `Insert` and `Contains` operations share an important property, namely, they only
-traverse a linear path from the root of the tree to the appropriate inner node or leaf. Since the tree nodes are actors
-which process messages one-by-one, no additional synchronization is needed between these operations. Removal in a
-binary tree unfortunately results in tree restructuring, which means that nodes would need to communicate and coordinate
-between each other (while additional operations arrive from the external world!).
-
-Therefore, instead of implementing the usual binary tree removal, in your solution you should use a flag that is stored
-in every tree node (`removed`) indicating whether the element in the node has been removed or not. This will result in a very
-simple implementation that is concurrent and correct with minimal effort. Unfortunately this decision results in the
-side effect that the tree set accumulates "garbage" (elements that have been removed) over time.
-
-#### Garbage Collection
-
-As we have seen, removal of entries can be implemented simply by using a removal flag with the
-added cost of growing garbage over time. To overcome this limitation you will need to implement a "garbage collection"
-feature. Whenever your binary tree set receives a `GC` message, it should clean up all the removed elements, while
-additional operations might arrive from the external world.
-
-The garbage collection task can be implemented in two steps. The first subtask is to implement an internal
-`CopyTo` operation on the binary tree that copies all its non-removed contents from the binary tree to a provided new one. This
-implementation can assume that no operations arrive while the copying happens (i.e. the tree is protected from modifications
-while copying takes places).
-
-The second part of the implementation is to implement garbage collection in the manager (`BinaryTreeSet`) by using the copy operation.
-The newly constructed tree should replace the old one and all actors from the old one should be stopped.
-Since copying assumes no other concurrent operations, the manager should handle the case when operations arrive while still
-performing the copy in the background. It is your responsibility to implement the manager in such a way that the fact
-that garbage collection happens is invisible from the outside (of course additional delay is allowed).
-For the sake of simplicity, your implementation should ignore GC requests that arrive while garbage collection is taking place.
-
-#### Ordering Guarantees
-
-Replies to operations may be sent in any order but the contents of `ContainsResult` replies must obey the order of the
-operations. To illustrate what this means observe the following example:
-
-Client sends:
-
- Insert(testActor, id=100, elem=1)
- Contains(testActor, id=50, elem=2)
- Remove(testActor, id=10, elem=1)
- Insert(testActor, id=20, elem=2)
- Contains(testActor, id=80, elem=1)
- Contains(testActor, id=70, elem=2)
-
-Client receives:
-
- ContainsResult(id=70, true)
- OperationFinished(id=20)
- OperationFinished(id=100)
- ContainsResult(id=80, false)
- OperationFinished(id=10)
- ContainsResult(id=50, false)
-
-While the results seem "garbled", they actually strictly correspond to the order of the original operations. On
-closer examination you can observe that the order of original operations was [100, 50, 10, 20, 80, 70]. Now if you
-order the responses according to this sequence the result would be:
-
- Insert(testActor, id=100, elem=1) -> OperationFinished(id=100)
- Contains(testActor, id=50, elem=2) -> ContainsResult(id=50, false)
- Remove(testActor, id=10, elem=1) -> OperationFinished(id=10)
- Insert(testActor, id=20, elem=2) -> OperationFinished(id=20)
- Contains(testActor, id=80, elem=1) -> ContainsResult(id=80, false)
- Contains(testActor, id=70, elem=2) -> ContainsResult(id=70, true)
-
-As you can see, the responses the client received are the same, hence they must have been executed sequentially,
-and only the responses have arrived out of order. Thus, the responses obey the semantics of sequential operations
- -- it is simply their arrival order is not
-defined. You might find it easier for testing to use sequential identifiers for the operations, since that makes it
-easier to follow the sequence of responses.
-
-You might also note that out-of-order responses can only happen if the client does not wait for each individual answer
-before continuing with sending operations.
-
-While this loose ordering guarantee on responses might look strange at first, it will significantly simplify the
-implementation of the binary tree and you are encouraged to make full use of it.
-
-#### Your task
-
-You can find code stubs in the file `BinaryTreeSet.scala` which provides you with the API as described above,
-the `BinaryTreeSet` and `BinaryTreeNode` classes. The `BinaryTreeSet` represents the whole binary tree.
-This is also the only actor that is explicitly created by the user and the only actor the user sends messages to.
-
-You can implement as many or as few message handlers as you like and you can add additional variables or helper
-functions. We provide suggestions in your code stub, marked with the comment `optional`, but you are free to use
-it fully or partially; the optional elements are not part of the tested API.
-
-To see a binary tree in operation check our provided tests in `BinaryTreeSuite.scala`. Note in particular
-that it is the user who triggers garbage collection by sending a `GC` message (for the sake of simplicity of this exercise).
-
-Don't forget to make sure that no `Operation` messages interfere during garbage collection and that the user does
-not receive any messages that may result from the copying process. To achieve this, put any incoming operation message
-into a queue during the garbage collection process. When the copying process is finished, re-send the queued messages
-to the tree root and clear the queue.
-
-The following may be useful for your implementation:
-
- * Another way to stop an actor, besides the `stop` method you have seen, is to send it a `PoisonPill` message.
- * `context.parent` returns the ActorRef of the actor which created the current actor (i.e. its parent).
- * If you see a log message like the following
-
- [INFO] [11/21/2013 14:04:13.237] [PostponeSpec-akka.actor.default-dispatcher-2] [akka://PostponeSpec/deadLetters] Message [actorbintree.BinaryTreeSet$OperationFinished] from Actor[akka://PostponeSpec/user/$e/my-actor#-1012560631] to Actor[akka://PostponeSpec/deadLetters] was not delivered. [1] dead letters encountered.
-
- it means that one of your messages (here the OperationFinished) message was not delivered from actor `my-actor` to actor `deadLetters`—the latter is where actors forward their messages after they terminate.
- You should check that you do not stop actors prematurely.
-
-*A word on Actor counts:*
-
- * The grader verifies that enough Actors are created when inserting elements.
- * The grader also verifies that enough Actors are stopped in response to a `GC` command (i.e. for those elements that were previously marked removed from the set).
diff --git a/labs/lab8-wikipedia/README.md b/labs/lab8-wikipedia/README.md
deleted file mode 100644
index 707cd13dc80d1dc9dd4d19511a6764779d0af8d1..0000000000000000000000000000000000000000
--- a/labs/lab8-wikipedia/README.md
+++ /dev/null
@@ -1,177 +0,0 @@
-# Wikipedia
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b wikipedia git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-wikipedia
-```
-
-## Useful links
-
- * [The API documentation of Spark](http://spark.apache.org/docs/latest/api/scala/org/apache/spark/index.html)
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-For this assignment, you will need to download the wikipedia dataset (68 MB):
-
-[http://alaska.epfl.ch/~dockermoocs/bigdata/wikipedia-grading.dat](http://alaska.epfl.ch/~dockermoocs/bigdata/wikipedia-grading.dat)
-
-and place it in the folder: `src/main/resources/wikipedia` in your
-project directory.
-
-In this assignment, you will get to know Spark by exploring full-text Wikipedia
-articles.
-
-Gauging how popular a programming language is important for companies judging
-whether or not they should adopt an emerging programming language. For that reason,
-industry analyst firm RedMonk has bi-annually computed a ranking of programming
-language popularity using a variety of data sources, typically from websites like
-GitHub and StackOverflow. See their
-[top-20 ranking for June 2016](http://redmonk.com/sogrady/2016/07/20/language-rankings-6-16/)
-as an example.
-
-In this assignment, we'll use our full-text data from Wikipedia to produce a
-rudimentary metric of how popular a programming language is, in an effort to see
-if our Wikipedia-based rankings bear any relation to the popular Red Monk rankings.
-
-You'll complete this exercise on just one node (your laptop).
-
-## Set up Spark
-
-For the sake of simplified logistics, we'll be running Spark in "local" mode. This
-means that your full Spark application will be run on one node, locally, on your
-laptop.
-
-To start, we need a `SparkContext`. A `SparkContext` is the
-"handle" to your cluster. Once you have a `SparkContext`, you can use it
-to create and populate RDDs with data.
-
-To create a `SparkContext`, you need to first create a
-`SparkConf` instance. A `SparkConf` represents the
-configuration of your Spark application. It's here that you must specify that you
-intend to run your application in "local" mode. You must also name your Spark
-application at this point. For help, see the
-[Spark API documentation](http://spark.apache.org/docs/latest/api/scala/org/apache/spark/index.html).
-
-Configure your cluster to run in local mode by implementing `val conf`
-and `val sc`.
-
-## Read-in Wikipedia Data
-
-There are several ways to read data into Spark. The simplest way to read in data
-is to convert an existing collection in memory to an RDD using the
-`parallelize` method of the Spark context.
-
-We have already implemented a method `parse` in the object
-`WikipediaData` object that parses a line of the dataset and turns it into a `WikipediaArticle`.
-
-Create an `RDD` (by implementing `val wikiRdd`) which contains
-the `WikipediaArticle` objects of `articles`.
-
-## Compute a ranking of programming languages
-
-We will use a simple metric for determining the popularity of a programming
-language: the number of Wikipedia articles that mention the language at least once.
-
-### Rank languages attempt #1: rankLangs
-
-**Computing** `occurrencesOfLang`
-
-Start by implementing a helper method `occurrencesOfLang` which computes
-the number of articles in an `RDD` of type `RDD[WikipediaArticles]`
-that mention the given language at least once. For the sake of simplicity we check
-that it least one word (delimited by spaces) of the article text is equal to the given
-language.
-
-**Computing the ranking,** `rankLangs`
-
-Using `occurrencesOfLang`, implement a method `rankLangs` which
-computes a list of pairs where the second component of the pair is the number of
-articles that mention the language (the first component of the pair is the name of
-the language).
-
-An example of what `rankLangs` might return might look like this, for
-example:
-
-```scala
-List(("Scala", 999999), ("JavaScript", 1278), ("LOLCODE", 982), ("Java", 42))
-```
-
-The list should be sorted in descending order. That is, according to this ranking,
-the pair with the highest second component (the count) should be the first element
-of the list.
-
-Pay attention to roughly how long it takes to run this part! (It should take
-tens of seconds.)
-
-### Rank languages attempt #2: rankLangsUsingIndex
-
-**Compute an inverted index**
-
-An inverted index is an index data structure storing a mapping from content,
-such as words or numbers, to a set of documents. In particular, the purpose of
-an inverted index is to allow fast full text searches. In our use-case, an
-inverted index would be useful for mapping from the names of programming
-languages to the collection of Wikipedia articles that mention the name at
-least once.
-
-To make working with the dataset more efficient and more convenient, implement
-a method that computes an "inverted index" which maps programming language names
-to the Wikipedia articles on which they occur at least once.
-
-Implement method `makeIndex` which returns an RDD of the following type:
-`RDD[(String, Iterable[WikipediaArticle])]`. This RDD contains pairs,
-such that for each language in the given `langs` list there is at most
-one pair. Furthermore, the second component of each pair (the `Iterable`)
-contains the `WikipediaArticles` that mention the language at least once.
-
-_Hint: You might want to use methods **`flatMap`** and
-**`groupByKey`** on **`RDD`** for this part._
-
-**Computing the ranking, `rankLangsUsingIndex`**
-
-Use the `makeIndex` method implemented in the previous part to
-implement a faster method for computing the language ranking.
-
-Like in part 1, `rankLangsUsingIndex` should compute a list of pairs
-where the second component of the pair is the number of articles that mention
-the language (the first component of the pair is the name of the language).
-
-Again, the list should be sorted in descending order. That is, according to
-this ranking, the pair with the highest second component (the count) should
-be the first element of the list.
-
-_Hint: method **`mapValues`** on **`PairRDD`** could be useful
-for this part._
-
-_Can you notice a performance improvement over attempt #1? Why?_
-
-### Rank languages attempt #3: rankLangsReduceByKey
-
-In the case where the inverted index from above is _only_ used for computing
-the ranking and for no other task (full-text search, say), it is more efficient
-to use the `reduceByKey` method to compute the ranking directly,
-without first computing an inverted index. Note that the `reduceByKey`
-method is only defined for RDDs containing pairs (each pair is interpreted as
-a key-value pair).
-
-Implement the `rankLangsReduceByKey` method, this time computing the
-ranking without the inverted index, using `reduceByKey`.
-
-Like in part 1 and 2, `rankLangsReduceByKey` should compute a list
-of pairs where the second component of the pair is the number of articles that
-mention the language (the first component of the pair is the name of the language).
-
-Again, the list should be sorted in descending order. That is, according to
-this ranking, the pair with the highest second component (the count) should
-be the first element of the list.
-
-_Can you notice an improvement in performance compared to measuring both the
-computation of the index and the computation of the ranking as we did in
-attempt #2? If so, can you think of a reason?_
diff --git a/labs/lab9-stackoverflow/README.md b/labs/lab9-stackoverflow/README.md
deleted file mode 100644
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--- a/labs/lab9-stackoverflow/README.md
+++ /dev/null
@@ -1,283 +0,0 @@
-# StackOverflow
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b stackoverflow git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-stackoverflow
-```
-
-## Useful links
-
- * [The API documentation of Spark](http://spark.apache.org/docs/latest/api/scala/org/apache/spark/index.html)
- * [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
- * [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
-
-**If you have issues with the IDE, try [reimporting the
-build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
-if you still have problems, use `compile` in sbt instead.**
-
-## Introduction
-
-For this assignment, you will need to download the stackoverflow dataset (84 MB):
-
-[http://alaska.epfl.ch/~dockermoocs/bigdata/stackoverflow-grading.csv](http://alaska.epfl.ch/~dockermoocs/bigdata/stackoverflow-grading.csv)
-
-and place it in the folder: `src/main/resources/stackoverflow` in your
-project directory.
-
-The overall goal of this assignment is to implement a distributed k-means algorithm
-which clusters posts on the popular question-answer platform StackOverflow
-according to their score. Moreover, this clustering should be executed in parallel
-for different programming languages, and the results should be compared.
-
-The motivation is as follows: StackOverflow is an important source of documentation.
-However, different user-provided answers may have very different ratings (based on
-user votes) based on their perceived value. Therefore, we would like to look at the
-distribution of questions and their answers. For example, how many highly-rated
-answers do StackOverflow users post, and how high are their scores? Are there big
-differences between higher-rated answers and lower-rated ones?
-
-Finally, we are interested in comparing these distributions for different
-programming language communities. Differences in distributions could reflect
-differences in the availability of documentation. For example, StackOverflow could
-have better documentation for a certain library than that library's API
-documentation. However, to avoid invalid conclusions we will focus on the
-well-defined problem of clustering answers according to their scores.
-
-## The Data
-
-You are given a CSV (comma-separated values) file with information about
-StackOverflow posts. Each line in the provided text file has the following format:
-
-```scala
-<postTypeId>,<id>,[<acceptedAnswer>],[<parentId>],<score>,[<tag>]
-```
-
-A short explanation of the comma-separated fields follows.
-
-```scala
-<postTypeId>: Type of the post. Type 1 = question,
- type 2 = answer.
-
-<id>: Unique id of the post (regardless of type).
-
-<acceptedAnswer>: Id of the accepted answer post. This
- information is optional, so maybe be missing
- indicated by an empty string.
-
-<parentId>: For an answer: id of the corresponding
- question. For a question:missing, indicated
- by an empty string.
-
-<score>: The StackOverflow score (based on user
- votes).
-
-<tag>: The tag indicates the programming language
- that the post is about, in case it's a
- question, or missing in case it's an answer.
-```
-
-You will see the following code in the main class:
-
-```scala
-val lines = sc.textFile("src/main/resources/stackoverflow/stackoverflow-grading.csv")
-val raw = rawPostings(lines)
-val grouped = groupedPostings(raw)
-val scored = scoredPostings(grouped)
-val vectors = vectorPostings(scored)
-```
-
-It corresponds to the following steps:
-
-1. `lines`: the lines from the csv file as strings
-2. `raw`: the raw Posting entries for each line
-3. `grouped`: questions and answers grouped together
-4. `scored`: questions and scores
-5. `vectors`: pairs of (language, score) for each question
-
-The first two methods are given to you. You will have to implement the rest.
-
-## Data processing
-
-We will now look at how you process the data before applying the kmeans algorithm.
-
-### Grouping questions and answers
-
-The first method you will have to implement is `groupedPostings`:
-
-```scala
-val grouped = groupedPostings(raw)
-```
-
-In the `raw` variable we have simple postings, either questions or
-answers, but in order to use the data we need to assemble them together. Questions
-are identified using a `postTypeId == 1`. Answers to a question with
-`id == QID` have (a) `postTypeId == 2` and (b) `parentId == QID`.
-
-Ideally, we want to obtain an RDD with the pairs of `(Question, Iterable[Answer])`.
-However, grouping on the question directly is expensive (can you imagine why?),
-so a better alternative is to match on the QID, thus producing an
-`RDD[(QID, Iterable[(Question, Answer))]`.
-
-To obtain this, in the `groupedPostings` method, first filter the
-questions and answers separately and then prepare them for a `join` operation by
-extracting the QID value in the first element of a tuple. Then, use one of the
-`join` operations (which one?) to obtain an `RDD[(QID, (Question, Answer))]`.
-Then, the last step is to obtain an `RDD[(QID, Iterable[(Question, Answer)])]`.
-How can you do that, what method do you use to group by the key of a pair RDD?
-
-Finally, in the description we used QID, Question and Answer types, which we've
-defined as type aliases for `Posting`s and `Int`s. The full list of type aliases is
-available in `StackOverflow.scala`:
-
-```scala
-type Question = Posting
-type Answer = Posting
-type QID = Int
-type HighScore = Int
-type LangIndex = Int
-```
-
-The above information should allow you to implement the `groupedPostings`
-method. Its signature is:
-
-```scala
-def groupedPostings(postings: RDD[Posting]):
- RDD[(QID, Iterable[(Question, Answer)])]
-```
-
-### Computing Scores
-
-Second, implement the `scoredPostings` method, which should return an
-RDD containing pairs of (a) questions and (b) the score of the answer with the
-highest score (note: this does **_not_** have to be the answer marked as
-`acceptedAnswer`!). The type of this scored RDD is:
-
-```scala
-val scored: RDD[(Question, HighScore)] = ???
-```
-
-For example, the `scored` RDD should contain the following tuples:
-
-```scala
-((1, 6, None, None, 140, Some(CSS)), 67)
-((1, 42, None, None, 155, Some(PHP)), 89)
-((1, 72, None, None, 16, Some(Ruby)), 3)
-((1, 126, None, None, 33, Some(Java)), 30)
-((1, 174, None, None, 38, Some(C#)), 20)
-```
-
-_Hint: use the provided **`answerHighScore`** given in **`scoredPostings`**._
-
-### Creating vectors for clustering
-
-Next, we prepare the input for the clustering algorithm. For this, we transform
-the `scored` RDD into a `vectors` RDD containing the vectors
-to be clustered. In our case, the vectors should be pairs with two components
-(in the listed order!):
-
-- Index of the language (in the `langs` list) multiplied by the
- `langSpread` factor.
-- The highest answer score (computed above).
-
-The `langSpread` factor is provided (set to 50000). Basically, it makes
-sure posts about different programming languages have at least distance 50000
-using the distance measure provided by the `euclideanDist` function.
-You will learn later what this distance means and why it is set to this value.
-
-The type of the `vectors` RDD is as follows:
-
-```scala
-val vectors: RDD[(LangIndex, HighScore)] = ???
-```
-
-For example, the `vectors` RDD should contain the following tuples:
-
-```scala
-(350000, 67)
-(100000, 89)
-(300000, 3)
-(50000, 30)
-(200000, 20)
-```
-
-Implement this functionality in method `vectorPostings` and by using
-the given `firstLangInTag` helper method.
-
-_(Idea for test: **`scored`** RDD should have 1042132 entries)_
-
-## Kmeans Clustering
-
-```scala
-val means = kmeans(sampleVectors(vectors), vectors)
-```
-
-Based on these initial means, and the provided variables `converged`
-method, implement the K-means algorithm by iteratively:
-
-- pairing each vector with the index of the closest mean (its cluster);
-- computing the new means by averaging the values of each cluster.
-
-To implement these iterative steps, use the provided functions
-`findClosest`, `averageVectors`, and `euclideanDistance`.
-
-**Note 1:**
-
-In our tests, convergence is reached after 44 iterations (for `langSpread = 50000`)
-and in 104 iterations (for `langSpread = 1`), and for the first iterations the
-distance kept growing. Although it may look like something is wrong, this is the
-expected behavior. Having many remote points forces the kernels to shift quite a
-bit and with each shift the effects ripple to other kernels, which also move around,
-and so on. Be patient, in 44 iterations the distance will drop from over 100000 to
-13, satisfying the convergence condition.
-
-If you want to get the results faster, feel free to downsample the data (each
-iteration is faster, but it still takes around 40 steps to converge):
-
-```scala
-val scored = scoredPostings(grouped).sample(true, 0.1, 0)
-```
-
-However, keep in mind that we will test your assignment on the full data set. So
-that means you can downsample for experimentation, but make sure your algorithm
-works on the full data set when you submit for grading.
-
-**Note 2:**
-
-The variable `langSpread` corresponds to how far away are languages from
-the clustering algorithm's point of view. For a value of 50000, the languages are
-too far away to be clustered together at all, resulting in a clustering that only
-takes scores into account for each language (similarly to partitioning the data
-across languages and then clustering based on the score). A more interesting (but
-less scientific) clustering occurs when `langSpread` is set to 1 (we can't
-set it to 0, as it loses language information completely), where we cluster according
-to the score. See which language dominates the top questions now?
-
-## Computing Cluster Details
-
-After the call to kmeans, we have the following code in method `main`:
-
-```scala
-val results = clusterResults(means, vectors)
-printResults(results)
-```
-
-Implement the `clusterResults` method, which, for each cluster, computes:
-
-- (a) the dominant programming language in the cluster;
-- (b) the percent of answers that belong to the dominant language;
-- (c) the size of the cluster (the number of questions it contains);
-- (d) the median of the highest answer scores.
-
-Once this value is returned, it is printed on the screen by the `printResults`
-method.
-
-## Questions
-
-- Do you think that partitioning your data would help?
-- Have you thought about persisting some of your data? Can you think of why persisting your data in memory may be helpful for this algorithm?
-- Of the non-empty clusters, how many clusters have "Java" as their label (based on the majority of questions, see above)? Why?
-- Only considering the "Java clusters", which clusters stand out and why?
-- How are the "C# clusters" different compared to the "Java clusters"?
-
-**Hint:** if you break the grader's time or memory constraints, think of how partitioning or persisting could, if at all, help you gain some performance. Please note that our grader only runs unit tests against your methods. It won't run the **main** method, so make sure to place any caching or partitioning code outside the **main**.
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