Presentation
Benchmarking Cutting-Edge Scientific Error-Bounded Lossy Compressors on Correlation-Based Rate-Distortion
DescriptionScientific error-bounded lossy compressors are widely used to reduce storage and I/O costs in large-scale scientific computing tasks. It is critical to benchmark those compressors to help users understand their performance. Nevertheless, when evaluating the decompressed data quality, existing benchmarks mainly focus on error-value-based data quality metrics such as PSNR, while correlational metrics such as SSIM, Error Autocorrelation, and the Pearson Coefficients are also important metrics. We benchmark seven compressors on six representative scientific datasets, evaluating diverse data quality metrics such as PSNR, SSIM, Error Autocorrelation, and Pearson Coefficient. Our results show that each existing compressor exhibits diverged performances over different metrics, and no single compressor can be advantageous on all metrics in one dataset or across all datasets in one metric. Comparing the performances of compressors on different quality metrics, we deliver some important takeaways and suggestions on how to select scientific error-bounded lossy compressors based on user requirements.
Event Type
Workshop
TimeMonday, 17 November 202511:20am - 11:45am CST
Location265
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