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The rapid growth of high-resolution scientific simulations and observation systems is generating massive spatiotemporal datasets, making efficient, error-bounded compression increasingly important. Meanwhile, decoder-only large language…

Machine Learning · Computer Science 2025-11-06 Guozhong Li , Muhannad Alhumaidi , Spiros Skiadopoulos , Panos Kalnis

The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is…

Machine Learning · Computer Science 2023-11-08 Jinyang Liu , Sheng Di , Sian Jin , Kai Zhao , Xin Liang , Zizhong Chen , Franck Cappello

Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compressor has been considered one of…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-11-15 Xin Liang , Kai Zhao , Sheng Di , Sihuan Li , Robert Underwood , Ali M. Gok , Jiannan Tian , Junjing Deng , Jon C. Calhoun , Dingwen Tao , Zizhong Chen , Franck Cappello

Data management is becoming increasingly important in dealing with the large amounts of data produced by large-scale scientific simulations and instruments. Existing multilevel compression algorithms offer a promising way to manage…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-11-12 Xin Liang , Ben Whitney , Jieyang Chen , Lipeng Wan , Qing Liu , Dingwen Tao , James Kress , Dave Pugmire , Matthew Wolf , Norbert Podhorszki , Scott Klasky

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-09-07 Jiannan Tian , Sheng Di , Xiaodong Yu , Cody Rivera , Kai Zhao , Sian Jin , Yunhe Feng , Xin Liang , Dingwen Tao , Franck Cappello

This paper introduces a novel technique to preserve spectral features in lossy compression based on a novel fast Fourier correction algorithm\added{ for regular-grid data}. Preserving both spatial and frequency representations of data is…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-01-06 Congrong Ren , Robert Underwood , Sheng Di , Emrecan Kutay , Zarija Lukic , Aylin Yener , Franck Cappello , Hanqi Guo

This paper presents error-bounded lossy compression tailored for particle datasets from diverse scientific applications in cosmology, fluid dynamics, and fusion energy sciences. As today's high-performance computing capabilities advance,…

Information Theory · Computer Science 2024-04-05 Congrong Ren , Sheng Di , Longtao Zhang , Kai Zhao , Hanqi Guo

Today's scientific simulations, for example in the high-performance exascale sector, produce huge amounts of data. Due to limited I/O bandwidth and available storage space, there is the necessity to reduce scientific data of high…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-07-25 N. Böing , J. Holke , C. Hergl , L. Spataro , G. Gassner , A. Basermann

Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalidating artifacts. Drawing on results from compressed sensing…

Machine Learning · Computer Science 2024-05-24 Matthias Chung , Rick Archibald , Paul Atzberger , Jack Michael Solomon

Error-bounded lossy compression has been a critical technique to significantly reduce the sheer amounts of simulation datasets for high-performance computing (HPC) scientific applications while effectively controlling the data distortion…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-08-23 Tripti Agarwal , Sheng Di , Jiajun Huang , Yafan Huang , Ganesh Gopalakrishnan , Robert Underwood , Kai Zhao , Xin Liang , Guanpeng Li , Franck Cappello

The generation of voluminous scientific data poses significant challenges for efficient storage, transfer, and analysis. Recently, error-bounded lossy compression methods emerged due to their ability to achieve high compression ratios while…

Machine Learning · Computer Science 2025-05-13 Guozhong Li , Muhannad Alhumaidi , Spiros Skiadopoulos , Ibrahim Hoteit , Panos Kalnis

The Morse-Smale complex is a standard tool in visual data analysis. The classic definition is based on a continuous view of the gradient of a scalar function where its zeros are the critical points. These points are connected via gradient…

Computational Geometry · Computer Science 2024-09-10 Son Le Thanh , Michael Ankele , Tino Weinkauf

Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction or transformation techniques to compress the data. Previous…

Machine Learning · Computer Science 2024-09-30 Youyuan Liu , Wenqi Jia , Taolue Yang , Miao Yin , Sian Jin

Because of the vast volume of data being produced by today's scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data size and the I/O burden. However, for large-scale cosmology…

Information Theory · Computer Science 2017-08-08 Dingewn Tao , Sheng Di , Zizhong Chen , Franck Cappello

Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential growth in data production is surpassing the advancements in…

Machine Learning · Computer Science 2024-09-10 Xiao Li , Jaemoon Lee , Anand Rangarajan , Sanjay Ranka

Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access decompression are critical features that enable on-demand…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-09-03 Daoce Wang , Pascal Grosset , Jesus Pulido , Jiannan Tian , Tushar M. Athawale , Jinda Jia , Baixi Sun , Boyuan Zhang , Sian Jin , Kai Zhao , James Ahrens , Fengguang Song

Modern scientific simulations generate massive volumes of data, creating significant challenges for I/O and storage systems. Error-bounded lossy compression (EBLC) offers a solution by reducing data set sizes while preserving data quality…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-06-16 Grant Wilkins , Sheng Di , Jon C. Calhoun , Robert Underwood , Franck Cappello

With the ever-increasing execution scale of high performance computing (HPC) applications, vast amounts of data are being produced by scientific research every day. Error-bounded lossy compression has been considered a very promising…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-10-24 Jinyang Liu , Sheng Di , Kai Zhao , Xin Liang , Zizhong Chen , Franck Cappello

Error-bounded lossy compression has been identified as a promising solution for significantly reducing scientific data volumes upon users' requirements on data distortion. For the existing scientific error-bounded lossy compressors, some of…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-09-26 Jinyang Liu , Sheng Di , Kai Zhao , Xin Liang , Sian Jin , Zizhe Jian , Jiajun Huang , Shixun Wu , Zizhong Chen , Franck Cappello

With ever-increasing volumes of scientific data produced by HPC applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-01-08 Dingwen Tao , Sheng Di , Xin Liang , Zizhong Chen , Franck Cappello