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Using C++ AMP to Accelerate HPC Applications on Multiple Platforms

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High Performance Computing (ISC High Performance 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9945))

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Abstract

Many high-end HPC systems support accelerators in their compute nodes to target a variety of workloads including high-performance 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.

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This paper is authored by an employee(s) of the United States Government and is in the public domain. Non-exclusive copying or redistribution is allowed, provided that the article citation is given and the authors and agency are clearly identified as its source.

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Acknowledgements

This material is based upon work supported by the U.S. Department of Energy, Office of science, and this research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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Correspondence to M. Graham Lopez .

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Lopez, M.G., Bergstrom, C., Li, Y.W., Elwasif, W., Hernandez, O. (2016). Using C++ AMP to Accelerate HPC Applications on Multiple Platforms. In: Taufer, M., Mohr, B., Kunkel, J. (eds) High Performance Computing. ISC High Performance 2016. Lecture Notes in Computer Science(), vol 9945. Springer, Cham. https://doi.org/10.1007/978-3-319-46079-6_38

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  • DOI: https://doi.org/10.1007/978-3-319-46079-6_38

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