8 papers
FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression
Guoxi Liu, Yuxiao Li, Congrong Ren +6
Modern scientific simulations generate massive volumes of data, making lossy compression essential for efficient storage and transmission. However, preserving critical quantities o…
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
Arham Khan, Robert Underwood, Carlo Siebenschuh +7
Contemporary large language model (LLM) training pipelines require the assembly of internet-scale databases full of text data from a variety of sources (e.g., web, academic, and pu…
pMSz: A Distributed Parallel Algorithm for Correcting Extrema and Morse Smale Segmentations in Lossy Compression
Yuxiao Li, Mingze Xia, Xin Liang +7
Lossy compression, widely used by scientists to reduce data from simulations, experiments, and observations, can distort features of interest even under bounded error. Such distort…
FFCz: Fast Fourier Correction for Spectrum-Preserving Lossy Compression of Scientific Data
Congrong Ren, Robert Underwood, Sheng Di +5
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}. Preserv…
DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
Khondoker Mirazul Mumenin, Robert Underwood, Dong Dai +4
Error-bounded lossy compression techniques have become vital for scientific data management and analytics, given the ever-increasing volume of data generated by modern scientific s…
To Compress or Not To Compress: Energy Trade-Offs and Benefits of Lossy Compressed I/O
Grant Wilkins, Sheng Di, Jon C. Calhoun +2
Modern scientific simulations generate massive volumes of data, creating significant challenges for I/O and storage systems. Error-bounded lossy compression (EBLC) offers a solutio…