Tools for top-down performance analysis of GPU-accelerated applications

Abstract

This paper describes extensions to Rice University's HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit's measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance metrics. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit's hpcprof constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grain analysis and tuning, HPCToolkit attributes GPU performance metrics to source lines and loops. Also, HPCToolkit uses GPU PC samples to derive and attribute a collection of useful GPU performance metrics. We illustrate HPCToolkit's new capabilities for analyzing GPU- accelerated applications with three case studies.