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20242026
most citedBoosting Scientific Error-Bounded Lossy Compression through Optimized Synergistic Lossy-Lossless Orchestration

6 citations · 7 across the 4 of their papers we have counts for

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cs.DC20256 cited

Boosting Scientific Error-Bounded Lossy Compression through Optimized Synergistic Lossy-Lossless Orchestration

Shixun Wu, Jinwen Pan, Jinyang Liu +8

As high-performance computing architectures evolve, more scientific computing workflows are being deployed on advanced computing platforms such as GPUs. These workflows can produce…

cs.DC2025

GPZ: GPU-Accelerated Lossy Compressor for Particle Data

Ruoyu Li, Yafan Huang, Longtao Zhang +10

Particle-based simulations and point-cloud applications generate massive, irregular datasets that challenge storage, I/O, and real-time analytics. Traditional compression technique…

cs.DC2025

IPComp: Interpolation Based Progressive Lossy Compression for Scientific Applications

Zhuoxun Yang, Sheng Di, Longtao Zhang +6

Compression is a crucial solution for data reduction in modern scientific applications due to the exponential growth of data from simulations, experiments, and observations. Compre…

cs.DC2025

ZCCL: Significantly Improving Collective Communication With Error-Bounded Lossy Compression

Jiajun Huang, Sheng Di, Xiaodong Yu +12

With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communication turns out to be a critic…

cs.DC20241 cited

HoSZp: An Efficient Homomorphic Error-bounded Lossy Compressor for Scientific Data

Tripti Agarwal, Sheng Di, Jiajun Huang +7

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

cs.DC2024

A Survey on Error-Bounded Lossy Compression for Scientific Datasets

Sheng Di, Jinyang Liu, Kai Zhao +23

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…