To Use or Not to Use: CPUs' Cache Optimization Techniques on GPGPUs

TL;DR

This paper investigates the applicability of CPU cache optimization techniques to GPGPU architectures, revealing that some techniques improve GPGPU performance while others may hinder it.

Contribution

It explores the adaptation of CPU cache optimization techniques to GPGPUs, providing insights into their effectiveness and suitability for high-performance computing.

Findings

Some CPU cache techniques improve GPGPU performance

Other techniques negatively impact GPGPU performance

Performance effects vary depending on the technique used

Abstract

General Purpose Graphic Processing Unit(GPGPU) is used widely for achieving high performance or high throughput in parallel programming. This capability of GPGPUs is very famous in the new era and mostly used for scientific computing which requires more processing power than normal personal computers. Therefore, most of the programmers, researchers and industry use this new concept for their work. However, achieving high-performance or high-throughput using GPGPUs are not an easy task compared with conventional programming concepts in the CPU side. In this research, the CPU's cache memory optimization techniques have been adopted to the GPGPU's cache memory to identify rare performance improvement techniques compared to GPGPU's best practices. The cache optimization techniques of blocking, loop fusion, array merging and array transpose were tested on GPGPUs for finding suitability of these techniques. Finally, we identified that some of the CPU cache optimization techniques go well with the cache memory system of the GPGPU and shows performance improvements while some others show the opposite effect on the GPGPUs compared with the CPUs.