SC22 Proceedings

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

ACM Student Research Competition Poster Archive

Spline-Interpolation-Based Lossy Compression for Scientific Data

Student: Jiannan Tian (Indiana University)
Supervisor: Dingwen Tao (Indiana University)

Abstract: Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors such as cuSZ have been developed. In order to improve the data quality and the compression ratio while maintaining high throughput, an interpolation-based spline method is introduced, inspired by the existing CPU prototype. In this work, We present (1) an efficient GPU implementation of the 3D interpolative spline prediction method, (2) a finer-grained data chunking approach using anchor points to leverage the modern GPU architecture, and (3) an in-depth analysis of how such anchor point affects the error formation and the compression ratio, and (4) a preliminary result in performance on the state-of-the-art modern GPUs. Our solution can achieve 1) a higher compression ratio than the previous default prediction method in cuSZ, and 2) the overall comparable data quality and compression ratio with the CPU prototype.

ACM-SRC Semi-Finalist: no

Poster: PDF
Poster Summary: PDF

Back to Poster Archive Listing