The power of GPUs is giving rise to heterogeneous parallel computing, with new demands on programming environments, runtime systems, and tools to deliver high-performing applications. This paper studies the problems associated with performance measurement of heterogeneous machines with GPUs. A heterogeneous computation model and alternative host-GPU measurement approaches are discussed to set the stage for reporting new capabilities for heterogeneous parallel performance measurement in three leading HPC tools: PAPI, Vampir, and the TAU Performance System. Our work leverages the new CUPTI tool support in NVIDIA's CUDA device library. Heterogeneous benchmarks from the SHOC suite are used to demonstrate the measurement methods and tool support.
['Allen D. Malony', 'Scott Biersdorff', 'Sameer Shende', 'Heike Jagode', 'Stanimire Tomov', 'Guido Juckeland', 'Robert Dietrich', 'Duncan Poole', 'Christopher J. Lamb']
Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs