labs/lab1-parallel-box-blur-filter/README.md

-# Parallel Box Blur Filter
-
-## Setup
-
-Let's upgrade the IDE support first, close VSCode if it's open and run:
-
-```shell
-code --force --install-extension scalameta.metals
-```
-
-Use the following commands to make a fresh clone of your repository:
-
-```
-git clone -b scalashop git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-scalashop
-```
-
-## 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.**
-
-## Setup scalashop
-
-In this assignment, we will implement a box blur filter, used in applications
-like Adobe® PhotoShop® to blur images.
-For the purposes of this assignment, a filter is an algorithm that takes an
-input image and transforms it in some way into an output image.
-The box blur filter outputs an image in which every pixel has an average value
-of the surrounding pixels from the original image.
-The box blur filter is an example of an *embarrassingly parallel* problem -- no
-or very little effort is required to separate it into parallel tasks.
-Every pixel of the output image can be computed independently of the other
-pixels, and in parallel.
-
-We will proceed in four steps. First, we will implement the kernel of the box
-blur filter, a method used to compute a single pixel of the output image.
-Then we will implement two versions of the parallel box blur, and measure the
-performance difference.
-Finally, we will try out our parallel box blur implementations on real images,
-using ScalaShop -- the image manipulation tool that would impress even
-the Adobe® folks.
-
-By the time you are finished with this assignment, you will:
-
-- understand how the box blur filter works
-- be able to recognize embarrassingly parallel problems
-- know how to measure the performance of a parallel algorithm
-- see how to agglomerate parallel computations into batches
-- better understand how memory access patterns affect performance of parallel
-  algorithms
-
-
-## Preliminaries
-
-Before we begin, we need to cover some basic data types and helper methods.
-These utilities have already been implemented in the package object (in the file `package.scala`) for this
-exercise.
-First, we will use the `RGBA` type to refer to the value of an image pixel.
-We will limit ourselves to 32-bit pixel depth images, so we define `RGBA` to
-be equal to the 32-bit integer type:
-
-```scala
-type RGBA = Int
-```
-
-Why do we call this type `RGBA`?
-This is because each pixel value is composed from 4 components - red, green,
-blue and alpha, where alpha denotes the amount of transparency in the
-respective pixel.
-These components are referred to as *channels*.
-The value of each channel is at least 0 and at most 255.
-
-We can extract the red, green, blue and alpha channel using the following
-utility methods, which use bit masking and bit shifting:
-
-```scala
-def red(c: RGBA): Int = (0xff000000 & c) >>> 24
-def green(c: RGBA): Int = (0x00ff0000 & c) >>> 16
-def blue(c: RGBA): Int = (0x0000ff00 & c) >>> 8
-def alpha(c: RGBA): Int = (0x000000ff & c) >>> 0
-```
-
-Similarly, given the values of the four channels, we can obtain the pixel value
-like this:
-
-```scala
-def rgba(r: Int, g: Int, b: Int, a: Int): RGBA =
-  (r << 24) | (g << 16) | (b << 8) | (a << 0)
-```
-
-Now that we know how to manipulate individual pixels, we can define our image
-type `Img`:
-
-```scala
-class Img(val width: Int, val height: Int) {
-  private val data = new Array[RGBA](width * height)
-  def apply(x: Int, y: Int): RGBA = data(y * width + x)
-  def update(x: Int, y: Int, c: RGBA): Unit = data(y * width + x) = c
-}
-```
-
-The image is a two-dimensional entity -- to refer to a pixel in an image, we
-need to specify an `x` and `y` component.
-On the other hand, the underlying memory model is one-dimensional -- a single
-offset value specifies the position in the array.
-When we store the image into memory, we need to map between the
-two-dimensional image model and the one-dimensional memory model.
-We will do this by storing consecutive rows of the image one after another, as
-illustrated in the following figure:
-
-![mapping](mapping.png)
-
-Thus, the offset of a pixel at coordinates `x` and `y`, is equal to
-`y * width + x`, where `width` is the number of pixels in a single row.
-Although there are other mappings used in practice, we will restrict to this
-simple mapping throughout the exercise.
-
-To ensure that the value of the `x` and `y` coordinates are confined to the
-dimensions of the image, namely width and height, occasionally we have to call
-the `clamp` method:
-
-```scala
-def clamp(v: Int, min: Int, max: Int): Int
-```
-
-Finally, we will use the `task` construct to start parallel computations.
-Every `task` construct invocation returns an object on which we can call the
-`join` method.
-Calling the `join` method blocks the execution of the program until the parallel
-computation ends.
-Below, we calculate the expression `1 + 1` in parallel to the main program:
-
-```scala
-val computation = task {
-   val result = 1 + 1
-   println("Done!")
-   result
-}
-println("About to wait for some heavy calculation...")
-computation.join()
-```
-
-We now have everything we need to start implementing the parallel box filter.
-
-
-## The Box Blur Filter Kernel
-
-In the first part of the assignment, we will implement the box blur filter
-kernel method.
-A kernel is a method that computes the resulting value of a single pixel.
-The kernel method is typically computationally cheap and is not worth
-parallelizing its implementation.
-However, as we will later see, we can apply the same kernel method to different
-pixels in parallel.
-
-![kernel](kernel.png)
-
-The `boxBlurKernel` method takes the source image `src`, coordinates `x` and `y`
-of the pixel, and the `radius` of the blur.
-It returns the resulting average value of the surrounding pixels.
-We compute the average value by separating the pixel into four channels,
-computing the average of each of the channels,
-and using the four average values to produce the final pixel value.
-In the previous figure, the `radius` parameter is equal to `1` and the average
-is computed from `9` pixels.
-
-You can find its signature in the package object of this assignment:
-
-    def boxBlurKernel(src: Img, x: Int, y: Int, radius: Int): RGBA
-
-Implement the `boxBlurKernel` method.
-Use two nested `while`-loops.
-Make sure that the pixels at the image edges are affected only by the
-pixels inside the image (hint: use the `clamp` method from the package object).
-
-
-## Vertical Stripping Box Blur
-
-We can now implement the parallel box blur filter.
-Note that the `boxBlurKernel` method is relatively inexpensive.
-Executing `boxBlurKernel` might be much faster than starting a parallel
-computation, so having a separate parallel computation for the value of each
-pixel would be far too expensive.
-For this reason, we want to batch together many `boxBlurKernel` calls, and have
-a smaller number of parallel tasks.
-This is sometimes referred to as *agglomeration*, and is present in many
-parallel algorithm implementations.
-
-There are many different ways we can do agglomeration for the parallel box blur.
-One is to divide the image into a fixed number of equally wide vertical strips.
-For each strip, we start a parallel task, and wait for their completion.
-Within each strip, we traverse the pixels going from the top to the bottom of
-the image, as illustrated in the following figure:
-
-![stripping](vertical.png)
-
-We start by implementing the sequential `blur` method in the
-`VerticalBoxBlur.scala` source file, which takes the source image `src`, the
-destination image `dst`, the starting (included) and ending (excluded) `x` coordinates
-(i.e, column indices) of the strip, called `from` and `end`, and the blur `radius`.
-The `blur` method blurs the pixels from the `src` image and writes them to the
-`dst` image:
-
-```scala
-def blur(src: Img, dst: Img, from: Int, end: Int, radius: Int): Unit
-```
-
-The implementation of `blur` should rely on the previously defined `boxBlurKernel`.
-
-Then, implement the `parBlur` method, which divides the image into `numTasks` vertical strips and runs each task in parallel:
-
-```scala
-def parBlur(src: Img, dst: Img, numTasks: Int, radius: Int): Unit
-```
-
-Use Scala ranges to create a list of splitting points
-(hint: use the `by` method on ranges).
-Then use collection combinators on the list of splitting points to create a list
-of start and end tuples, one for each strip
-(hint: use the `zip` and `tail` methods).
-Finally, use the `task` construct to start a parallel task for each strip,
-and then call `join` on each task to wait for its completion.
-
-### Benchmarking VerticalBoxBlur
-
-Before running the benchmarking program, go to the top of `VerticalBoxBlur.scala`
-and replace the line:
-```scala
-    Key.verbose -> true
-```
-by:
-```scala
-    Key.verbose -> false
-```
-
-
-Now you can run the `VerticalBoxBlur` program with the following sbt command:
-
-```
-> runMain scalashop.VerticalBoxBlurRunner
-```
-
-Change the number of tasks and the radius parameter.
-How does the performance change?
-
-
-## Horizontal Stripping Box Blur
-
-In this part of the exercise we will pick an alternative agglomeration for the
-box blur algorithm.
-Instead of dividing the image into vertical strips, we will divide it into
-horizontal strips in a similar way:
-
-![stripping](horizontal.png)
-
-We implement the two methods, `blur` and `parBlur` in a similar way as before:
-
-```scala
-def blur(src: Img, dst: Img, from: Int, end: Int, radius: Int): Unit
-def parBlur(src: Img, dst: Img, numTasks: Int, radius: Int): Unit
-```
-
-Note that the arguments `from` (included) and `end` (excluded) this time denote the values of the
-`y` coordinate (i.e, row indices), and that we traverse the pixels left-to-right within each strip.
-
-### Benchmarking HorizontalBoxBlur
-
-Before running the benchmarking program, go to the top of `HorizontalBoxBlur.scala`
-and replace the line:
-```scala
-    Key.verbose -> true
-```
-by:
-```scala
-    Key.verbose -> false
-```
-
-
-Now you can now run the `HorizontalBoxBlur` program from sbt with:
-
-```
-> runMain scalashop.HorizontalBoxBlurRunner
-```
-
-If you implemented the two blur versions correctly, you should observe that the
-horizontal stripping is slightly faster.
-This is because the pixel traversal order visits the pixels which are closer
-together in memory (remember the mapping between the pixel coordinates and the
-memory addresses). As a result, each core can reuse some of the pixels that it
-fetched from the memory during the previous invocation of `boxBlurKernel`.
-The processor cores spend less time fetching pixels from memory and lower the
-pressure on the memory bus.
-
-
-## ScalaShop
-
-Now we have everything we need to start ScalaShop, from sbt run:
-
-```
-> runMain scalashop.ScalaShop
-```
-
-Change the blur implementation, parallelism level and blur radius, and study the
-effect your changes have on performance.
-
- * Which of the two blur implementations is faster?
- * For which values of the `radius` parameter is the difference most significant?
-   * Why?
- * What if we split the image into rectangles? Will this be faster?
-
-*"Adobe" and "Photoshop" are either registered trademarks or trademarks of Adobe
-Systems Incorporated in the United States and/or other countries.*

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