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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

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 is a critical technique for significantly reducing scientific data volumes. Compared to CPU-based compressors, GPU-based compressors exhibit substantially higher throughputs, fitting better for today's HPC…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-10-02 Jinyang Liu , Jiannan Tian , Shixun Wu , Sheng Di , Boyuan Zhang , Robert Underwood , Yafan Huang , Jiajun Huang , Kai Zhao , Guanpeng Li , Dingwen Tao , Zizhong Chen , 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

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

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

Compression is a crucial solution for data reduction in modern scientific applications due to the exponential growth of data from simulations, experiments, and observations. Compression with progressive retrieval capability allows users to…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-04-08 Zhuoxun Yang , Sheng Di , Longtao Zhang , Ruoyu Li , Ximiao Li , Jiajun Huang , Jinyang Liu , Franck Cappello , Kai Zhao

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

As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-05-16 Chengming Zhang , Sian Jin , Tong Geng , Jiannan Tian , Ang Li , Dingwen Tao

Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during simulations or instrument data acquisitions. Not only can it significantly…

Machine Learning · Computer Science 2023-10-24 Jinyang Liu , Sheng Di , Kai Zhao , Sian Jin , Dingwen Tao , Xin Liang , Zizhong Chen , Franck Cappello

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for postanalysis. Because supercomputers and…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-09-22 Jiannan Tian , Sheng Di , Kai Zhao , Cody Rivera , Megan Hickman Fulp , Robert Underwood , Sian Jin , Xin Liang , Jon Calhoun , Dingwen Tao , Franck Cappello

Error-bounded lossy compression is essential for managing the massive data volumes produced by large-scale HPC simulations. While state-of-the-art compressors such as SZ and ZFP provide strong numerical error guarantees, they often fail to…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-02-20 Tripti Agarwal , Sheng Di , Xin Liang , Zhaoyuan Su , Yuxiao Li , Ganesh Gopalakrishnan , Hanqi Guo , Franck Cappello

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

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

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

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…

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…

Today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost.…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-05-04 Boyuan Zhang , Jiannan Tian , Sheng Di , Xiaodong Yu , Yunhe Feng , Xin Liang , Dingwen Tao , 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

Error-controlled lossy compression has been studied for years because of extremely large volumes of data being produced by today's scientific simulations. None of existing lossy compressors, however, allow users to fix the peak…

Information Theory · Computer Science 2018-07-17 Dingwen Tao , Sheng Di , Xin Liang , Zizhong Chen , Franck Cappello
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