Spline-Interpolation-Based Lossy Compression for Scientific Data
DescriptionError-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.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
TimeThursday, 17 November 20228:30am - 5pm CST
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