GPU610/DPS915 CUDA PI

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CUDA PI Calcuation (Monte Carlo)

Team Pi CUDA

Welcome to GPU610AA Fall 2013 Team Pi CUDA Page.

My name is Peter Huang and I'm a student in the GPU610 class for the Fall Semester of 2013. Having no background whatsoever in parallel programming, I've decided to choose something that is out of my scope of understanding and interest (video game programming) to challenge myself. Thus, I've decided to investigate the benefits of parallel programming applied to the Monte Carlo statistical method to approximating the value of pi.

Announcements

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Team Members

1. Peter Huang

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Progress

Assignment 1

Introduction

For the initial profiling, I've decided to investigate the Monte Carlo Statistics Methodology of approximating the value of Pi. A brief explanation of Monte Carlo Pi calculation can be found here: https://www.youtube.com/watch?v=VJTFfIqO4TU

Source File(s)

Link: https://drive.google.com/file/d/0B8GUuIUqdEJES3VEOGRnYmRNaEk

Code Snippet

   // loops through user amount of rounds of sets of points
   for(i = 0; i < points; i++)
   {
      x = randNum();
      y = randNum();
      
      // check if point resides within the circle
      if (((x*x) + (y*y)) <= 1.0)
      {
         score++;
      }     
   }
   // calculate pi
   pi = 4.0 * (float)score/(float)points;

Compilation and Running

Serial Results

Assignment 2

Introduction

In Phase 2, I've parallelized the serial program to run on a custom kernel on a CUDA-enabled device.

Compilation and Running

Parallel (CUDA) Results

Serial VS CUDA

Source File(s)

Link: https://drive.google.com/file/d/0B8GUuIUqdEJEbDBRNkhWYnpGSnM

Agenda

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Progress

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Meetings

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Discussion

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