16885

Design and optimization of a portable LQCD Monte Carlo code using OpenACC

Claudio Bonati, Enrico Calore, Simone Coscetti, Massimo D’Elia, Michele Mesiti, Francesco Negro, Sebastiano Fabio Schifano, Giorgio Silvi, Raffaele Tripiccione
Universita di Pisa and INFN Sezione di Pisa, Largo Pontecorvo 3, I-56127 Pisa, Italy
arXiv:1701.00426 [hep-lat], (2 Jan 2017)

@article{bonati2017design,

   title={Design and optimization of a portable LQCD Monte Carlo code using OpenACC},

   author={Bonati, Claudio and Calore, Enrico and Coscetti, Simone and D’Elia, Massimo and Mesiti, Michele and Negro, Francesco and Schifano, Sebastiano Fabio and Silvi, Giorgio and Tripiccione, Raffaele},

   year={2017},

   month={jan},

   archivePrefix={"arXiv"},

   primaryClass={hep-lat}

}

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The present panorama of HPC architectures is extremely heterogeneous, ranging from traditional multi-core CPU processors, supporting a wide class of applications but delivering moderate computing performance, to many-core GPUs, exploiting aggressive data-parallelism and delivering higher performances for streaming computing applications. In this scenario, code portability (and performance portability) become necessary for easy maintainability of applications; this is very relevant in scientific computing where code changes are very frequent, making it tedious and prone to error to keep different code versions aligned. In this work we present the design and optimization of a state-of-the-art production-level LQCD Monte Carlo application, using the directive-based OpenACC programming model. OpenACC abstracts parallel programming to a descriptive level, relieving programmers from specifying how codes should be mapped onto the target architecture. We describe the implementation of a code fully written in OpenACC, and show that we are able to target several different architectures, including state-of-the-art traditional CPUs and GPUs, with the same code. We also measure performance, evaluating the computing efficiency of our OpenACC code on several architectures, comparing with GPU-specific implementations and showing that a good level of performance-portability can be reached.
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