Speaker
Description
The High-Luminosity Large Hadron Collider (HL-LHC) is expected to produce data at the exabyte scale, motivating the exploration of new methods for reducing data volumes. Error-bounded lossy compression has been adopted in many scientific domains as an effective strategy for reducing storage and I/O costs without compromising the quality of downstream analyses.
However, selecting an appropriate compressor for a dataset is not straightforward. Error-bounded lossy compression encompasses a diverse set of techniques. The performance of these techniques may depend on both the statistical properties of the target data and/or tunable parameters.
We present LossBench, a command-line utility for benchmarking error-bounded lossy compressors, with support for ROOT TTrees. LossBench takes a compressor configuration and benchmarks it on data from a specified ROOT file, writing results in the JSONL format. Recorded metrics include compression ratio, compression and decompression throughput, and several common distortion measures. Optionally, decompressed data can be written to a new TTree to assess the impact of compression on physics analyses. LossBench's framework enables the integration of new compressors and metrics to allow rapid experimentation with novel/custom compression techniques.
We demonstrate the use of LossBench through a case study applying SZ3, a state-of-the-art error-bounded lossy compressor, to ATLAS open data. Across a range of SZ3 configurations, we observe that certain parameter choices achieve higher compression ratios than lossless methods while maintaining low distortion. These results offer insight into which lossy compression strategies may be appropriate for HEP datasets and highlight key considerations when evaluating lossy compression for particle physics.