ValueExpert: exploring value patterns in GPU-accelerated applications
Keren Zhou, Yueming Hao, John Mellor‐Crummey, Xiaozhu Meng, Xu Liu · 2022
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.