Franck Cappello

Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization

2017

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 for …

Optimizing Lossy Compression with Adjacent Snapshots for N-body Simulation Data

2018

Today's N-body simulations are producing extremely large amounts of data. The Hardware/Hybrid Accelerated Cosmology Code (HACC), for example, may simulate trillions of particles, producing tens of petabytes of data to store in a parallel file …

cuSZ

2020

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 HPC applications are becoming …

gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters

2024

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 integrate lossy compression into GPU-aware collectives, which can lead to serious …