Accelerating multigrid-based hierarchical scientific data refactoring on GPUs

Document Type

Conference Proceeding

Publication Date

5-1-2021

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.

Identifier

85113528397 (Scopus)

ISBN

[9781665440660]

Publication Title

Proceedings 2021 IEEE 35th International Parallel and Distributed Processing Symposium IPDPS 2021

External Full Text Location

https://doi.org/10.1109/IPDPS49936.2021.00095

First Page

859

Last Page

868

Grant

DE-AC05-00OR22725

Fund Ref

U.S. Department of Energy

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