A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels

TL;DR

This paper presents an automated tool designed to analyze complex GPU kernels and suggest source-code optimizations, aiming to simplify programming and improve performance on future heterogeneous computing systems.

Contribution

The paper introduces a novel automatic optimization suggestion tool specifically for complex GPU kernels, addressing the challenge of programming increasingly heterogeneous systems.

Findings

The tool effectively identifies optimization opportunities in GPU kernels.

It improves performance and energy efficiency through recommended code modifications.

The approach demonstrates potential to assist programmers in complex parallel computing environments.

Abstract

Future computing systems, from handhelds to supercomputers, will undoubtedly be more parallel and heterogeneous than todays systems to provide more performance and energy efficiency. Thus, GPUs are increasingly being used to accelerate general purpose applications, including applications with data dependent, irregular control flow and memory access patterns. However, the growing complexity, exposed memory hierarchy, incoherence, heterogeneity, and parallelism will make accelerator based systems progressively more difficult to program. In the foreseeable future, the vast majority of programmers will no longer be able to extract additional performance or energy savings from next generation systems be-cause the programming will be too difficult. Automatic performance analysis and optimization recommendation tools have the potential to avert this situation. They embody expert knowledge and make it available to software developers when needed. In this paper, we describe and evaluate such a tool.