ValueExpert: Exploring value patterns in GPU-Accelerated applications

Abstract

General-purpose GPUs have become common in modern computing systems to accelerate applications in many domains, including machine learning, high-performance computing, and autonomous driving. However, inefficiencies abound in GPU-Accelerated applications, which prevent them from obtaining bare-metal performance. Performance tools play an important role in understanding performance inefficiencies in complex code bases. Many GPU performance tools pinpoint time-consuming code and provide high-level performance insights but overlook one important performance issue-value-related inefficiencies, which exist in many GPU code bases. In this paper, we present ValueExpert, a novel tool to pinpoint value-related inefficiencies in GPU applications. ValueExpert monitors application execution to capture values produced and used by each load and store operation in GPU kernels, recognizes multiple value patterns, and provides intuitive optimization guidance. We address systemic challenges in collecting, maintaining, and analyzing voluminous performance data from many GPU threads to make ValueExpert applicable to complex applications. We evaluate ValueExpert on a wide range of well-Tuned benchmarks and applications, including PyTorch, Darknet, LAMMPS, Castro, and many others. ValueExpert is able to identify previously unknown performance issues and provide suggestions for nontrivial performance improvements with typically less than five lines of code changes. We verify our optimizations with application developers and upstream fixes to their repositories.