{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:19:46Z","timestamp":1773317986394,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T00:00:00Z","timestamp":1763164800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC05-00OR22725, DE-SC0021399"],"award-info":[{"award-number":["DE-AC05-00OR22725, DE-SC0021399"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. National Science Foundation","award":["OAC-2311756, OAC-2313122, OAC-2442627, OAC-2311757, OAC-2144403"],"award-info":[{"award-number":["OAC-2311756, OAC-2313122, OAC-2442627, OAC-2311757, OAC-2144403"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,16]]},"DOI":"10.1145\/3712285.3759845","type":"proceedings-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T16:05:39Z","timestamp":1762963539000},"page":"2076-2093","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3263-0087","authenticated-orcid":false,"given":"Yanliang","family":"Li","sequence":"first","affiliation":[{"name":"University of Oregon, Eugene, Oregon, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5687-4446","authenticated-orcid":false,"given":"Wenbo","family":"Li","sequence":"additional","affiliation":[{"name":"University of Kentucky, Lexington, Kentucky, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3570-4142","authenticated-orcid":false,"given":"Qian","family":"Gong","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7600-7976","authenticated-orcid":false,"given":"Qing","family":"Liu","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, Newark, New Jersey, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9647-542X","authenticated-orcid":false,"given":"Norbert","family":"Podhorszki","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3559-5772","authenticated-orcid":false,"given":"Scott","family":"Klasky","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0630-1600","authenticated-orcid":false,"given":"Xin","family":"Liang","sequence":"additional","affiliation":[{"name":"University of Kentucky, Lexington, Kentucky, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1905-9171","authenticated-orcid":false,"given":"Jieyang","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Oregon, Eugene, Oregon, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,11,15]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"[n. d.]. Aurora exscale system. https:\/\/www.alcf.anl.gov\/support-center\/aurora."},{"key":"e_1_3_3_2_3_2","unstructured":"[n. d.]. Frontier exscale supercomputer. https:\/\/www.olcf.ornl.gov\/frontier."},{"key":"e_1_3_3_2_4_2","unstructured":"[n. d.]. El Captain exscale system. https:\/\/asc.llnl.gov\/exascale\/el-capitan."},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Allison\u00a0H Baker Dorit\u00a0M Hammerling Sheri\u00a0A Mickelson Haiying Xu Martin\u00a0B Stolpe Phillipe Naveau Ben Sanderson Imme Ebert-Uphoff Savini Samarasinghe Francesco De\u00a0Simone et\u00a0al. 2016. Evaluating lossy data compression on climate simulation data within a large ensemble. Geoscientific Model Development 9 12 (2016) 4381\u20134403.","DOI":"10.5194\/gmd-9-4381-2016"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1290\/1\/012008"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63393-6_7"},{"key":"e_1_3_3_2_8_2","unstructured":"Robert Underwood Jon\u00a0C Calhoun Sheng Di and Franck Cappello. 2024. Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.15953 (2024)."},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Shaomeng Li Stanislaw Jaroszynski Scott Pearse Leigh Orf and John Clyne. 2019. Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10 9 (2019) 488.","DOI":"10.3390\/atmos10090488"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476179"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Victor\u00a0AP Magri and Peter Lindstrom. 2023. A general framework for progressive data compression and retrieval. IEEE Transactions on Visualization and Computer Graphics 30 1 (2023) 1358\u20131368.","DOI":"10.1109\/TVCG.2023.3327186"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC41406.2024.00092"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Gregory\u00a0K Wallace. 1992. The JPEG still picture compression standard. IEEE transactions on consumer electronics 38 1 (1992) xviii\u2013xxxiv.","DOI":"10.1109\/30.125072"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Majid Rabbani. 2002. JPEG2000: Image compression fundamentals standards and practice. Journal of Electronic Imaging 11 2 (2002) 286.","DOI":"10.1117\/1.1469618"},{"key":"e_1_3_3_2_15_2","unstructured":"[n. d.]. Summit exscale system. https:\/\/www.olcf.ornl.gov\/summit."},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Peter Deutsch. 1996. GZIP file format specification version 4.3. (1996).","DOI":"10.17487\/rfc1952"},{"key":"e_1_3_3_2_17_2","unstructured":"Yann Collet. [n. d.]. Zstandard - Real-time data compression algorithm. http:\/\/facebook.github.io\/zstd\/. Online."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Peter Lindstrom and Martin Isenburg. 2006. Fast and efficient compression of floating-point data. IEEE transactions on visualization and computer graphics 12 5 (2006) 1245\u20131250.","DOI":"10.1109\/TVCG.2006.143"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Sriram Lakshminarasimhan Neil Shah Stephane Ethier Seung-Hoe Ku Choong-Seock Chang Scott Klasky Rob Latham Rob Ross and Nagiza\u00a0F Samatova. 2013. ISABELA for effective in situ compression of scientific data. Concurrency and Computation: Practice and Experience 25 4 (2013) 524\u2013540.","DOI":"10.1002\/cpe.2887"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Peter Lindstrom. 2014. Fixed-rate compressed floating-point arrays. IEEE transactions on visualization and computer graphics 20 12 (2014) 2674\u20132683.","DOI":"10.1109\/TVCG.2014.2346458"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2017.115"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622520"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00145"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Mark Ainsworth Ozan Tugluk Ben Whitney and Scott Klasky. 2019. Multilevel techniques for compression and reduction of scientific data\u2014the multivariate case. SIAM Journal on Scientific Computing 41 2 (2019) A1278\u2013A1303.","DOI":"10.1137\/18M1166651"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Mark Ainsworth Ozan Tugluk Ben Whitney and Scott Klasky. 2020. Multilevel techniques for compression and reduction of scientific data\u2014The unstructured case. SIAM Journal on Scientific Computing 42 2 (2020) A1402\u2013A1427.","DOI":"10.1137\/19M1267878"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Xin Liang Ben Whitney Jieyang Chen Lipeng Wan Qing Liu Dingwen Tao James Kress David Pugmire Matthew Wolf Norbert Podhorszki et\u00a0al. 2021. Mgard+: Optimizing multilevel methods for error-bounded scientific data reduction. IEEE Trans. Comput. 71 7 (2021) 1522\u20131536.","DOI":"10.1109\/TC.2021.3092201"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Xin Liang Kai Zhao Sheng Di Sihuan Li Robert Underwood Ali\u00a0M Gok Jiannan Tian Junjing Deng Jon\u00a0C Calhoun Dingwen Tao et\u00a0al. 2022. Sz3: A modular framework for composing prediction-based error-bounded lossy compressors. IEEE Transactions on Big Data 9 2 (2022) 485\u2013498.","DOI":"10.1109\/TBDATA.2022.3201176"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00681"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"David\u00a0A Huffman. 1952. A method for the construction of minimum-redundancy codes. Proceedings of the IRE 40 9 (1952) 1098\u20131101.","DOI":"10.1109\/JRPROC.1952.273898"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Mark Ainsworth Ozan Tugluk Ben Whitney and Scott Klasky. 2018. Multilevel techniques for compression and reduction of scientific data\u2014the univariate case. Computing and Visualization in Science 19 5 (2018) 65\u201376.","DOI":"10.1007\/s00791-018-00303-9"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS54959.2023.00104"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Rafael Ballester-Ripoll Peter Lindstrom and Renato Pajarola. 2019. TTHRESH: Tensor compression for multidimensional visual data. IEEE transactions on visualization and computer graphics 26 9 (2019) 2891\u20132903.","DOI":"10.1109\/TVCG.2019.2904063"},{"key":"e_1_3_3_2_33_2","unstructured":"Jinyang Liu Sheng Di Kai Zhao Sian Jin Dingwen Tao Xin Liang Zizhong Chen and Franck Cappello. 2021. Exploring Autoencoder-Based Error-Bounded Compression for Scientific Data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2105.11730 (2021)."},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3410463.3414624"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS49936.2021.00095"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC41406.2024.00021"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Jieyang Chen Qian Gong Yanliang Li Xin Liang Lipeng Wan Qing Liu Norbert Podhorszki and Scott Klasky. 2025. HPDR: High-Performance Portable Scientific Data Reduction Framework. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2503.06322 (2025).","DOI":"10.1109\/IPDPS64566.2025.00101"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3295500.3356169"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Peter Lindstrom Jeffrey Hittinger James Diffenderfer Alyson Fox Daniel Osei-Kuffuor and Jeffrey Banks. 2025. ZFP: A compressed array representation for numerical computations. The International Journal of High Performance Computing Applications 39 1 (2025) 104\u2013122.","DOI":"10.1177\/10943420241284023"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS49936.2021.00097"},{"key":"e_1_3_3_2_41_2","unstructured":"Ana Balevic. 2009. Fine-Grain Parallelization of Entropy Coding on GPGPUs."},{"key":"e_1_3_3_2_42_2","unstructured":"[n. d.]. SDRBench. https:\/\/sdrbench.github.io."},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378449"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"crossref","unstructured":"PK Yeung DA Donzis and KR Sreenivasan. 2012. Dissipation enstrophy and pressure statistics in turbulence simulations at high Reynolds numbers. Journal of Fluid Mechanics 700 (2012) 5\u201315.","DOI":"10.1017\/jfm.2012.5"}],"event":{"name":"SC '25: The International Conference for High Performance Computing, Networking, Storage and Analysis","location":"St. Louis MO USA","acronym":"SC '25","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing"]},"container-title":["Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3712285.3759845","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3712285.3759845","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3712285.3759845","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:33:38Z","timestamp":1773254018000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3712285.3759845"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,15]]},"references-count":43,"alternative-id":["10.1145\/3712285.3759845","10.1145\/3712285"],"URL":"https:\/\/doi.org\/10.1145\/3712285.3759845","relation":{},"subject":[],"published":{"date-parts":[[2025,11,15]]},"assertion":[{"value":"2025-11-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}