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Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide…

The growing volume of scientific simulation data presents a significant challenge for storage and transfer. Error-bounded lossy compression has emerged as a critical solution for mitigating these challenges, providing a means to reduce data…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-01-29 Arshan Khan , Rohit Deshmukh , Ben O'Neill

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

Increasing data volumes from scientific simulations and instruments (supercomputers, accelerators, telescopes) often exceed network, storage, and analysis capabilities. The scientific community's response to this challenge is scientific…

As high-performance computing architectures evolve, more scientific computing workflows are being deployed on advanced computing platforms such as GPUs. These workflows can produce raw data at extremely high throughputs, requiring urgent…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-09-03 Shixun Wu , Jinwen Pan , Jinyang Liu , Jiannan Tian , Ziwei Qiu , Jiajun Huang , Kai Zhao , Xin Liang , Sheng Di , Zizhong Chen , Franck Cappello

Error-controlled lossy compressors have been widely used in scientific applications to reduce the unprecedented size of scientific data while keeping data distortion within a user-specified threshold. While they significantly mitigate the…

Databases · Computer Science 2026-03-27 Xuan Wu , Sheng Di , Tripti Agarwal , Kai Zhao , Xin Liang , Franck Cappello

Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compression…

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

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

Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we design a new error-controlled lossy compression algorithm…

Information Theory · Computer Science 2017-06-14 Dingwen Tao , Sheng Di , 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

Today's scientific high performance computing (HPC) applications or advanced instruments are producing vast volumes of data across a wide range of domains, which introduces a serious burden on data transfer and storage. Error-bounded lossy…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-02-01 Xiaodong Yu , Sheng Di , Kai Zhao , jiannan Tian , Dingwen Tao , Xin Liang , Franck Cappello

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

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

With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-01-20 Robert Underwood , Sheng Di , Jon C. Calhoun , Franck Cappello

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

The rapid expansion of computational capabilities and the ever-growing scale of modern HPC systems present formidable challenges in managing exascale scientific data. Faced with such vast datasets, traditional lossless compression…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-04-23 Wenqi Jia , Sian Jin , Jinzhen Wang , Wei Niu , Dingwen Tao , Miao Yin

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

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

We explore an error-bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data,…

Graphics · Computer Science 2024-04-04 Congrong Ren , Xin Liang , Hanqi Guo
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