SC2025Top-tier venue
What to Support When You're Compressing: The State of Practice Gaps and Opportunities for Scientific Data Compression
Franck Cappello, Robert Underwood, Yuri Alexeev, Allison H. Baker, Ebru Bozdag, Martin Burtscher, Kyle Chard, Sheng Di, Kyle Gerard Felker, Paul Christopher O'Grady, Hanqi Guo, Yafan Huang
Abstract
Over the last nearly 20 years, lossy compression has become an essential aspect of HPC applications’ data pipelines, allowing them to overcome limitations in storage capacity and bandwidth and, in some cases, increase computational throughput and capacity. However, with the adoption of lossy compression comes the requirement to assess and control the impact lossy compression has on scientific outcomes. In this work, we take a major step forward in describing the state of practice and by characterizing workloads. We examine applications’ needs and compressors’ capabilities across 9 different supercomputing application domains. We present 24 takeaways that provide best practices for applications, operational impacts for facilities achieving compressed data, and gaps in application needs not addressed by production compressors that point towards opportunities for future compression research.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Related papers
- CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras CS-2Shihui Song, Yafan Huang, Peng Jiang, Xiaodong Yu et al.HPDC 2024 · 14 citations
- Bridging Information Theory and Practice for Scientific Lossy CompressionSujata Sinha, Sheng Di, Vishwas Rao, Robert Underwood et al.HPDC 2026
- GPU Lossy Compression for HPC Can Be Versatile and Ultra-FastYafan Huang, Sheng Di, Guanpeng Li, Franck CappelloSC 2025 · 6 citations
- Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5Sian Jin, Dingwen Tao, Houjun Tang, Sheng Di et al.SC 2022 · 15 citations
- cuSZ-i: High-Ratio Scientific Lossy Compression on GPUs with Optimized Multi-Level InterpolationJinyang Liu, Jiannan Tian, Shixun Wu, Sheng Di et al.SC 2024 · 17 citations
