Accelerating multigrid-based hierarchical scientific data refactoring on GPUs

Jieyang Chen, Lipeng Wan, Xin Liang, Ben Whitney, Qing Liu, David Pugmire, Nicholas Thompson, Jong Youl Choi, Matthew Wolf, Todd Munson, Ian Foster, Scott Klasky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and mathematically sound operations. However, the effective use of such representations has been hindered until now by the relatively high costs of creating, accessing, reducing, and otherwise operating on such representations. We describe here highly optimized data refactoring kernels for GPU accelerators that enable efficient creation and manipulation of data in multigrid-based hierarchical forms. We demonstrate that our optimized design can achieve up to 250 TB/s aggregated data refactoring throughput - 83% of theoretical peak - on 1024 nodes of the Summit supercomputer. We showcase our optimized design by applying it to a large-scale scientific visualization workflow and the MGARD lossy compression software.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages859-868
Number of pages10
ISBN (Electronic)9781665440660
DOIs
StatePublished - May 2021
Event35th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2021 - Virtual, Online
Duration: May 17 2021May 21 2021

Publication series

NameProceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021

Conference

Conference35th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2021
CityVirtual, Online
Period5/17/215/21/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

Keywords

  • Data refactoring
  • GPU
  • Multigrid

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