Zizhong Chen

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 …

Docker-Enabled Build and Execution Environment (BEE): an Encapsulated Environment Enabling HPC Applications Running Everywhere

2017 · arXiv (Cornell University)

Variations in High Performance Computing (HPC) system software configurations mean that applications are typically configured and built for specific HPC environments. Building applications can require a significant investment of time and effort for application users …

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 …

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 …