Using C++ AMP to accelerate HPC applications on multiple platforms

Abstract

Many high-end HPC systems support accelerators in their compute nodes to target a variety of workloads including highperformance computing simulations, big data / data analytics codes and visualization. To program both the CPU cores and attached accelerators, users now have multiple programming models available such as CUDA, OpenMP 4, OpenACC, C++14, etc., but some of these models fall short in their support for C++ on accelerators because they can have difficulty supporting advanced C++ features e.g. templating, class members, loops with iterators, lambdas, deep copy, etc. Usually, they either rely on unified memory, or the programming language is not aware of accelerators (e.g. C++14). In this paper, we explore a base-language solution called C++ Accelerated Massive Parallelism (AMP), which was developed by Microsoft and implemented by the PathScale ENZO compiler to program GPUs on a variety of HPC architectures including OpenPOWER and Intel Xeon. We report some prelminary in-progress results using C++ AMP to accelerate a matrix multiplication and quantum Monte Carlo application kernel, examining its expressiveness and performance using NVIDIA GPUs and the PathScale ENZO compiler. We hope that this preliminary report will provide a data point that will inform the functionality needed for future C++ standards to support accelerators with discrete memory spaces.