In-Depth Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations

TL;DR

This paper evaluates and enhances single-snapshot lossy compression techniques for N-body simulations, achieving significant I/O reduction and improved compression ratios through novel optimization methods tailored for cosmology and molecular dynamics models.

Contribution

The paper introduces novel optimization techniques for lossy compression in N-body simulations, improving compression ratios and I/O performance for cosmology and molecular dynamics models.

Findings

Compression ratio improved by 11% over second-best compressor.

I/O time reduced by 80% on 1024 cores.

Proposed methods outperform state-of-the-art compressors in rate-distortion.

Abstract

In situ lossy compression allowing user-controlled data loss can significantly reduce the I/O burden. For large-scale N-body simulations where only one snapshot can be compressed at a time, the lossy compression ratio is very limited because of the fairly low spatial coherence of the particle data. In this work, we assess the state-of-the-art single-snapshot lossy compression techniques of two common N-body simulation models: cosmology and molecular dynamics. We design a series of novel optimization techniques based on the two representative real-world N-body simulation codes. For molecular dynamics simulation, we propose three compression modes (i.e., best speed, best tradeoff, best compression mode) that can refine the tradeoff between the compression rate (a.k.a., speed/throughput) and ratio. For cosmology simulation, we identify that our improved SZ is the best lossy compressor with respect to both compression ratio and rate. Its compression ratio is higher than the second-best compressor by 11% with comparable compression rate. Experiments with up to 1024 cores on the Blues supercomputer at Argonne show that our proposed lossy compression method can reduce I/O time by 80% compared with writing data directly to a parallel file system and outperforms the second-best solution by 60%. Moreover, our proposed lossy compression methods have the best rate-distortion with reasonable compression errors on the tested N-body simulation data compared with state-of-the-art compressors.