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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.
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster