{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T08:01:13Z","timestamp":1768032073735,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["OAC-2003709"],"award-info":[{"award-number":["OAC-2003709"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["OAC-2104023"],"award-info":[{"award-number":["OAC-2104023"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["OAC-2311875"],"award-info":[{"award-number":["OAC-2311875"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"DOE U.S. Department of Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3625549.3658691","type":"proceedings-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T15:55:29Z","timestamp":1725033329000},"page":"309-321","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras CS-2"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9426-8591","authenticated-orcid":false,"given":"Shihui","family":"Song","sequence":"first","affiliation":[{"name":"University of Iowa, Iowa City, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7370-6766","authenticated-orcid":false,"given":"Yafan","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Iowa, Iowa City, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7743-6062","authenticated-orcid":false,"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Iowa, Iowa City, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6244-1264","authenticated-orcid":false,"given":"Xiaodong","family":"Yu","sequence":"additional","affiliation":[{"name":"Stevens Institute of Technology, Hoboken, New Jersey, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2791-0031","authenticated-orcid":false,"given":"Weijian","family":"Zheng","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9935-5674","authenticated-orcid":false,"given":"Sheng","family":"Di","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6690-194X","authenticated-orcid":false,"given":"Qinglei","family":"Cao","sequence":"additional","affiliation":[{"name":"Saint Louis University, St. Louis,, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6577-227X","authenticated-orcid":false,"given":"Yunhe","family":"Feng","sequence":"additional","affiliation":[{"name":"University of North Texas, Dallas, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3516-2192","authenticated-orcid":false,"given":"Zhen","family":"Xie","sequence":"additional","affiliation":[{"name":"Binghamton University, New York, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7890-3934","authenticated-orcid":false,"given":"Franck","family":"Cappello","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, United States of America"}]}],"member":"320","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n.d.]. Hurricane ISABEL simulation dataset in IEEE Visualization 2004 Test. http:\/\/vis.computer.org\/vis2004contest\/data.html"},{"key":"e_1_3_2_1_2_1","unstructured":"[n.d.]. NYX simulation. https:\/\/amrex-astro.github.io\/Nyx\/"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/18M1166651"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/0004-637X\/765\/1\/39"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1190\/1.1441434"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956761"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/1094342019853336"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2016.11"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2503210.2504566"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.newast.2015.06.003"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2010.579"},{"key":"e_1_3_2_1_12_1","volume-title":"Smart-DNN: Efficiently Reducing the Memory Requirements of Running Deep Neural Networks on Resource-constrained Platforms. In 2021 IEEE 39th International Conference on Computer Design (ICCD)","author":"Hu Zhenbo","unstructured":"Zhenbo Hu, Xiangyu Zou, Wen Xia, Yuhong Zhao, Weizhe Zhang, and Donglei Wu. 2021. Smart-DNN: Efficiently Reducing the Memory Requirements of Running Deep Neural Networks on Resource-constrained Platforms. In 2021 IEEE 39th International Conference on Computer Design (ICCD). IEEE, 533--541."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581784.3607048"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCGrid57682.2023.00066"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the International Conference on Parallel Architectures and Compilation Techniques. 185--197","author":"Jiang Peng","year":"2022","unstructured":"Peng Jiang, Yihua Wei, Jiya Su, Rujia Wang, and Bo Wu. 2022. SampleMine: A Framework for Applying Random Sampling to Subgraph Pattern Mining through Loop Perforation. In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques. 185--197."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/3574245.3574255"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3431379.3460653"},{"key":"e_1_3_2_1_18_1","volume-title":"Shuaiwen Leon Song, and Dingwen Tao","author":"Jin Sian","year":"2021","unstructured":"Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, and Dingwen Tao. 2021. Comet: a novel memory-efficient deep learning training framework by using error-bounded lossy compression. arXiv preprint arXiv:2111.09562 (2021)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-13-00255.1"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-648X\/aab9c3"},{"key":"e_1_3_2_1_21_1","unstructured":"Argonne National Laboratory. [n.d.]. SZp: An OpenMP-accelerated error-bounded lossy compressor on CPU. https:\/\/github.com\/szcompressor\/SZp-OMP"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS54959.2023.00104"},{"key":"e_1_3_2_1_23_1","volume-title":"Computer graphics forum","author":"Li Shaomeng","unstructured":"Shaomeng Li, Nicole Marsaglia, Christoph Garth, Jonathan Woodring, John Clyne, and Hank Childs. 2018. Data reduction techniques for simulation, visualization and data analysis. In Computer graphics forum, Vol. 37. Wiley Online Library, 422--447."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622520"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/TC.2021.3092201","article-title":"MGARD+: Optimizing multilevel methods for error-bounded scientific data reduction","volume":"71","author":"Liang Xin","year":"2021","unstructured":"Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, et al. 2021. MGARD+: Optimizing multilevel methods for error-bounded scientific data reduction. IEEE Trans. Comput. 71, 7 (2021), 1522--1536.","journal-title":"IEEE Trans. Comput."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3201176"},{"key":"e_1_3_2_1_27_1","unstructured":"Peter Lindstrom. [n.d.]. cuZFP. https:\/\/github.com\/LLNL\/zfp\/tree\/develop\/src\/cuda_zfp."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2014.2346458"},{"key":"e_1_3_2_1_29_1","volume-title":"2021 IEEE International Conference on Big Data (Big Data). IEEE, 2986--2991","author":"Liu Jinyang","year":"2021","unstructured":"Jinyang Liu, Sihuan Li, Sheng Di, Xin Liang, Kai Zhao, Dingwen Tao, Zizhong Chen, and Franck Cappello. 2021. Improving lossy compression for sz by exploring the best-fit lossless compression techniques. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2986--2991."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581784.3627042"},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings, 37th International Free Electron Laser Conference (FEL 2015)","author":"Marcus Gabriel","year":"2015","unstructured":"Gabriel Marcus, Y Ding, P Emma, Z Huang, J Qiang, T Raubenheimer, M Venturini, and L Wang. 2015. High fidelity start-to-end numerical particle simulations and performance studies for LCLS-II. In Proceedings, 37th International Free Electron Laser Conference (FEL 2015): Daejeon, Korea, August 23--28."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3577193.3593708"},{"key":"e_1_3_2_1_33_1","unstructured":"Etienne Robein. November 15 2016. EAGE E-Lecture: Reverse Time Migration: How Does It Work When To Use It."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3624062.3624116"},{"key":"e_1_3_2_1_35_1","volume-title":"The Exascale Era is Upon Us: The Frontier supercomputer may be the first to reach 1,000,000,000,000,000,000 operations per second","author":"Schneider David","year":"2022","unstructured":"David Schneider. 2022. The Exascale Era is Upon Us: The Frontier supercomputer may be the first to reach 1,000,000,000,000,000,000 operations per second. IEEE spectrum 59, 1 (2022), 34--35."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3577193.3593736"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3524059.3532384"},{"key":"e_1_3_2_1_38_1","volume-title":"Aurora: Argonne's next-generation exascale supercomputer. Technical Report. Argonne National Lab.(ANL)","author":"Stevens Rick","year":"2019","unstructured":"Rick Stevens, Jini Ramprakash, Paul Messina, Michael Papka, and Katherine Riley. 2019. Aurora: Argonne's next-generation exascale supercomputer. Technical Report. Argonne National Lab.(ANL), Argonne, IL (United States)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2017.115"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1177\/1094342017737147"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3332466.3374525"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3410463.3414624"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS49936.2021.00097"},{"key":"e_1_3_2_1_44_1","volume-title":"Image quality assessment: from error visibility to structural similarity","author":"Wang Zhou","year":"2004","unstructured":"Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13, 4 (2004), 600--612."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC41404.2022.00058"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3225058.3225076"},{"key":"e_1_3_2_1_47_1","volume-title":"2019 IEEE\/ACM Third Workshop on Deep Learning on Supercomputers (DLS). IEEE, 84--94","author":"Yin Junqi","year":"2019","unstructured":"Junqi Yin, Shubhankar Gahlot, Nouamane Laanait, Ketan Maheshwari, Jack Morrison, Sajal Dash, and Mallikarjun Shankar. 2019. Strategies to deploy and scale deep learning on the summit supercomputer. In 2019 IEEE\/ACM Third Workshop on Deep Learning on Supercomputers (DLS). IEEE, 84--94."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3502181.3531473"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588983.3596679"},{"key":"e_1_3_2_1_50_1","volume-title":"Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing. ACM, 129--142","author":"Zhang Boyuan","year":"2023","unstructured":"Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Yunhe Feng, Xin Liang, Dingwen Tao, and Franck Cappello. 2023. FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs. In Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing. ACM, 129--142."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3577193.3593706"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00145"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378449"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392688"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1177\/10943420231201154"}],"event":{"name":"HPDC '24: 33rd International Symposium on High-Performance Parallel and Distributed Computing","location":"Pisa Italy","acronym":"HPDC '24","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture","SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing"]},"container-title":["Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625549.3658691","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3625549.3658691","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:38Z","timestamp":1750287038000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625549.3658691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":55,"alternative-id":["10.1145\/3625549.3658691","10.1145\/3625549"],"URL":"https:\/\/doi.org\/10.1145\/3625549.3658691","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2024-08-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}