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For this assignment, I selected one application that I wished to parallelize. I profiled it to find the hotspot of the application and determine if it was feasible to speed up using the GPU.
My application that I selected is a PI approximation methodfunction. PI can be approximated in a number of ways however I chose to use the dartboard algorithm. Although not the fastest, the algorithm is very feasible to parallelize. The main idea behind it can be compared to a dartboard – you throw a random number (n) of darts at the board and note down the darts that have landed within it and those that have not. The image below demonstrates this concept:
[[File:dart.gif]]
When the target number is less than a million, the program runs in under a second. Every million items after that though took one more cumulative second then the previous. 1M = 1.104212 secs, 2M = 2.912168 secs, 3M = 5.026149 secs, 4M = 7.468770 secs, and 5M = 10.654563 secs. As you can see, while this is fairly efficient, the program could still use some help spreading the load around.
This seems like a pretty good candidate for a program to be parallelized as It calculates tons of numbers sequentially when ideally it could just do a bunch of them at a time sequenciallysequentially.
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'''Overall Decision'''
With the above two possible programs to parallelize, our team came to the decision to go with the estimation of Pi as our group project. We came to this decision because while generating prime numbers would be good, the Pi approximation function serves more purposes and would more likely show more interesting results when parallelized with the GPU.
=== Assignment 2 ===
=== Assignment 3 ===
