DOI: https://doi.org/10.15368/theses.2015.176
Available at: https://digitalcommons.calpoly.edu/theses/1525
Date of Award
12-2015
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
Advisor
John Y. Oliver
Abstract
ABSTRACT
Analyzing General-Purpose Computing Performance on GPU
Graphic Processing Unit (GPU) has become one of the most important components in modern computer systems. GPUs have evolved from a single -purpose graphic rendering hardware to a powerful processor that is capable of handling many different kinds of computing tasks. However, GPUs don’t perform well on every application, and it takes a lot of design effort to get good performance on a GPU.
This thesis aims to investigate the relative performance of a GPU vs. CPU. Design effort is held minimum for both CPU implementations and GPU implementations. Matrix multiplication, Advance Encryption Standard (AES) and 32-bit Cyclic Redundancy Check (CRC32) are implemented on both a CPU and GPU. Input data size is varied to test the performance of the CPU and the GPU. The GPU generally has better performance than the CPU for matrix multiplication and AES because of the applications' good instruction and data parallelism. CRC has very poor parallelism, so the CPU performs better. For very small data inputs, the CPU generally outperformed the GPU because of GPU memory transfer overhead.