2021/05/26 by Jieyang Chen, Lipeng Wan, Chen, Jieyang +23
Computer Science · #Advanced Data Storage Technologies #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Parallel #Parallel Computing and Optimization Techniques #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.2105.12764
arXiv admin note: text overlap with arXiv:2007.04457
arxiv created 2021/05/26 · openalex publication_date 2021/05/26 · arxiv updated 2021/05/28 · openalex created_date 2021/06/07 · openalex updated_date 2026/07/28
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth makes 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 264 TB/s aggregated data refactoring throughput -- 92% 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.