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Importance-based sampling methods, including our spatio-temporal hybrid data sampling method, are capable of resolving these bottlenecks. While our hybrid method has been shown to outperform existing methods, its effectiveness relies heavily on user parameters, such as histogram bins, error threshold, or number of regions. Moreover, the throughput it demonstrates must be higher to avoid becoming a bottleneck itself. In this article, we resolve both of these issues. First, we assess the effects of several user input parameters and detail techniques to help determine optimal parameters. Next, we detail and implement accelerated versions of our method using OpenMP and CUDA. Upon analyzing our implementations, we find 9.8\u00d7 to 31.5\u00d7 throughput improvements. Next, we demonstrate how our method can accept different base sampling algorithms and the effects these different algorithms have. Finally, we compare our sampling methods to the lossy compressor cuSZ in terms of data preservation and data movement. <\/jats:p>","DOI":"10.1177\/10943420231180504","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T19:46:00Z","timestamp":1685994360000},"page":"539-559","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Accelerated dynamic data reduction using spatial and temporal properties"],"prefix":"10.1177","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5630-049X","authenticated-orcid":false,"given":"Megan","family":"Hickman Fulp","sequence":"first","affiliation":[{"name":"Holcombe Department of Electrical and Computing Engineering, Clemson University, Clemson, SC, USA"}]},{"given":"Dakota","family":"Fulp","sequence":"additional","affiliation":[{"name":"Holcombe Department of Electrical and Computing Engineering, Clemson University, Clemson, SC, 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