296 citations · 1k across the 43 of their papers we have counts for
43 papers
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
Grant Wilkins, Sheng Di, Jon C. Calhoun +5
With the promise of federated learning (FL) to allow for geographically-distributed and highly personalized services, the efficient exchange of model updates between clients and se…
cuSZ-: High-Ratio Scientific Lossy Compression on GPUs with Optimized Multi-Level Interpolation
Jinyang Liu, Jiannan Tian, Shixun Wu +10
Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. Compared to CPU-based compressors, GPU-based compressors exhibit substan…
High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation
Jinyang Liu, Sheng Di, Kai Zhao +7
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…
Dynamic Quality Metric Oriented Error-bounded Lossy Compression for Scientific Datasets
Jinyang Liu, Sheng Di, Kai Zhao +3
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 l…
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
Jinyang Liu, Sheng Di, Sian Jin +4
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 usa…
gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters
Jiajun Huang, Sheng Di, Xiaodong Yu +11
GPU-aware collective communication has become a major bottleneck for modern computing platforms as GPU computing power rapidly rises. A traditional approach is to directly integrat…
AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications
Daoce Wang, Jesus Pulido, Pascal Grosset +11
As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences expo…
Optimizing Scientific Data Transfer on Globus with Error-bounded Lossy Compression
Yuanjian Liu, Sheng Di, Kyle Chard +2
The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area…
Black-Box Statistical Prediction of Lossy Compression Ratios for Scientific Data
Robert Underwood, Julie Bessac, David Krasowska +3
Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and mov…
FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs
Boyuan Zhang, Jiannan Tian, Sheng Di +5
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…
GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs
Boyuan Zhang, Jiannan Tian, Sheng Di +4
Today's graphics processing unit (GPU) applications produce vast volumes of data, which are challenging to store and transfer efficiently. Thus, data compression is becoming a crit…
An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression
Jiajun Huang, Sheng Di, Xiaodong Yu +11
With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communications turns out to be a criti…
Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5
Sian Jin, Dingwen Tao, Houjun Tang +4
Lossy compression is one of the most efficient solutions to reduce storage overhead and improve I/O performance for HPC applications. However, existing parallel I/O libraries canno…
ROIBIN-SZ: Fast and Science-Preserving Compression for Serial Crystallography
Robert Underwood, Chun Yoon, Ali Gok +2
Crystallography is the leading technique to study atomic structures of proteins and produces enormous volumes of information that can place strains on the storage and data transfer…
SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets
Xiaodong Yu, Sheng Di, Kai Zhao +4
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 serio…
SIMD Lossy Compression for Scientific Data
Griffin Dube, Jiannan Tian, Sheng Di +3
Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Data reduction techniques, such as lossy compress…
Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs
Cody Rivera, Sheng Di, Jiannan Tian +3
More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to…
Towards Aggregated Asynchronous Checkpointing
Mikaila J. Gossman, Bogdan Nicolae, Jon C. Calhoun +2
High-Performance Computing (HPC) applications need to checkpoint massive amounts of data at scale. Multi-level asynchronous checkpoint runtimes like VELOC (Very Low Overhead Checkp…
Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets
David Krasowska, Julie Bessac, Robert Underwood +3
Lossy compression plays a growing role in scientific simulations where the cost of storing their output data can span terabytes. Using error bounded lossy compression reduces the a…
SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors
Xin Liang, Kai Zhao, Sheng Di +9
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 spac…
Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling
Sian Jin, Sheng Di, Jiannan Tian +3
Error-bounded lossy compression is one of the most effective techniques for scientific data reduction. However, the traditional trial-and-error approach used to configure lossy com…
Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs
Jiannan Tian, Sheng Di, Xiaodong Yu +7
Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) archit…
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
Jinyang Liu, Sheng Di, Kai Zhao +5
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 instr…
VELOC: VEry Low Overhead Checkpointing in the Age of Exascale
Bogdan Nicolae, Adam Moody, Gregory Kosinovsky +2
Checkpointing large amounts of related data concurrently to stable storage is a common I/O pattern of many HPC applications. However, such a pattern frequently leads to I/O bottlen…