# Changes

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## Avengers

, 6 April
Assignment 2
In [https://computing.llnl.gov/tutorials/parallel_comp/#ExamplesArray Blaise Barney's Notes on Array Processing], an example of Array Processing is discussed. The example "demonstrates calculations on 2-dimensional array element; a function is evaluated on each array element."
I used the pseudo-code provided to create a program that creates a 2-dimensional array. The purpose of the program is to create and populate a 2-dimensional array of size n (provided by the user) with random numbers. In this case, the function that is applied to each element in the 2-dimensional array is 'std::rand()/100'. The code is available in the link below:
[https://github.com/jsidhu26/DPS915 Link to Array Processing code]
[[File:Array_Processing_-_Call_Graph.jpeg]]
From the call graph file, it is evident that the generateRandom() function is an obvious hotspot. It as it is hogging 100% of the execution time. The function consists of 2 for loops, one nested in the other, which makes the function have a Big-O notation of O(n^2).
The computations involved with each element in the array is independent from the rest of the elements, and therefore this function is a deserving candidate for parallelization. Additionally, the array elements can be evenly distributed into sub-arrays and a process can be assigned to each sub-array.
For Assignment 2, we decided to parallelize the application selected by Bruno.
In the code, the function that took up a significant amount of time was the calculateDimensions() function. The flat profile indicates that this function takes 97.67% of the execution time.

Link to code on GitHub: https://github.com/brucremo/DPS915
==== Identifying Parallelize-able Code ====
=== Assignment 3 ===
==== Using Shared Memory ====
To optimize our code, we used shared memory inside the kernel. For our purposes, allocating arrays in the kernel using shared memory required a constant value for the number of threads per block. This meant that the number of threads per block could not be calculated at run time. Instead, we set the number of threads per block to 1024 and declared it as a constant in the beginning of the application. This allowed us to use shared memory inside the kernel and optimize our application.

Below is a picture of the optimized kernel:

[[File:QuickerKernel.PNG]]

==== Results ====

On average, the run time for all 7 different problem sizes was reduced by approximately 216 milliseconds.

Below is a graph that illustrates the time saved on all 7 different problem sizes with the optimized kernel:

[[File:OptimizationGraph.PNG]]

==== Other Optimizations ====

Another optimization would be to optimize the original algorithm itself. The pow() function itself very expensive because it is software implemented. A better alternative would be to use hardware functions. In this case, it would be quicker to use the (*) operator and multiply the values with themselves as opposed to setting them to the power of 2.

[[File:OptimizedAlgorithm.PNG]]
To optimize our After modifying the codeto reflect this change, we used shared memory inside both the GPU and CPU methods are roughly equal to each other (in terms of execution time) when the kernelvalue of n is 800. This reduced the run time for each problem size
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