SC23 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

ACM Student Research Competition Poster Archive

ROI Preservation in Streaming Lossy Compression

Student: Avinash Kethineedi (Clemson University)
Supervisor: Jon Calhoun (Clemson University)

Abstract: Today’s state-of-the-art scientific high-performance computing (HPC) applications generate extensive data in diverse domains, placing a significant strain on data transfer and storage systems. Most compression algorithms are more computationally complex, requiring more processing power and time to compress and decompress data. However, these algorithms tend to achieve higher compression ratios resulting in smaller compressed data sizes. Real-time streaming applications demand high data throughput. Therefore, striking a right balance between compression efficiency and computational complexity is essential. This poster explores two key aspects: interpolation method of 'sz3' algorithm for data reconstruction and the application of 'szx' algorithm on a 'Region of Interest(ROI)' - where a lesser data distortion is needed. We perform a through evaluation using NYX scientific dataset. Experiments show that compression ratio is improved by ~2x. Compression and decompression rates are improved by ~5-7x when contiguous ROI is preserved and only certain recursive levels of sx3 are processed.

ACM-SRC Semi-Finalist: no

Poster: PDF
Poster Summary: PDF

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