high performance computing on graphics processing units: hgpu.org

Future computing systems, from handhelds to supercomputers, will undoubtedly be more parallel and heterogeneous than today’s 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 because 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. It quantifies performance characteristics of GPU code through profiling, employs machine learning models to estimate the suitability and benefit of several known source-code optimizations, ranks the optimizations, and suggests the most promising ones to the user if the expected speedup is sufficiently high.