The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: The 11th International Workshop on Data Analysis and Reduction for Big Scientific Data
Authors: Ziwei Qiu (University of Houston)
Abstract: Scientific 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.
Back to The 11th International Workshop on Data Analysis and Reduction for Big Scientific Data Archive Listing Back to Full Workshop Archive Listing