cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data

Abstract

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for postanalysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version, cuSZ, for one of the best error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data. Our contributions are fourfold. (1) We propose a dual-quantization scheme to entirely remove the data dependency in the prediction step of SZ such that this step can be performed very efficiently on GPUs. (2) We develop an efficient customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x, respectively, over the production version running on single and multiple CPU cores, respectively, while getting the same quality of reconstructed data. It also improves the compression ratio by up to 3.48x on the tested data compared with another state-of-the-art GPU supported lossy compressor.

13 pages, 8 figures, 9 tables, published in PACT '20

References in corpus (3)

• Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization
• DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression
• Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

Cited by in corpus (9)

• SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors
• FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs
• GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs
• Lossy compression of statistical data using quantum annealer
• What Operations can be Performed Directly on Compressed Arrays, and with What Error?
• COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
• Exploring Autoencoder-based Error-bounded Compression for Scientific Data
• STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data
• Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures