We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for applications on today’s and future HPC systems, we present a scaling law characterizing performance bottlenecks in state-of-the-art approaches for data reduction. Consequently, we propose, implement and verify multi-threaded data-transformations for the I/O library ADIOS as a feasible way to trade underutilized host-side compute potential on heterogeneous systems for reduced I/O latency.
CITATION STYLE
Huebl, A., Widera, R., Schmitt, F., Matthes, A., Podhorszki, N., Choi, J. Y., … Bussmann, M. (2017). On the scalability of data reduction techniques in current and upcoming HPC systems from an application perspective. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10524 LNCS, pp. 15–29). Springer Verlag. https://doi.org/10.1007/978-3-319-67630-2_2
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