cuSZ-I: High-Fidelity Error-Bounded Lossy Compression for Scientific Data on GPUs
CoRR(2023)
摘要
Error-bounded lossy compression is a critical technique for significantly
reducing scientific data volumes. Compared to CPU-based scientific compressors,
GPU-accelerated compressors exhibit substantially higher throughputs, which can
thus better adapt to GPU-based scientific simulation applications. However, a
critical limitation still lies in all existing GPU-accelerated error-bounded
lossy compressors: they suffer from low compression ratios, which strictly
restricts their scope of usage. To address this limitation, in this paper, we
propose a new design of GPU-accelerated scientific error-bounded lossy
compressor, namely cuSZ-I, which has achieved the following contributions: (1)
A brand new GPU-customized interpolation-based data pre-diction method is
raised in cuSZ-I for extensively improving the compression ratio and the
decompression data quality. (2) The Huffman encoding module in cuSZ-I has been
improved for both efficiency and stability. (3) cuSZ-I is the first work to
integrate the highly effective NVIDIA bitcomp lossless compression module to
maximally boost the compression ratio for GPU-accelerated lossy compressors
with nearly negligible speed degradation. In experimental evaluations, with the
same magnitude of compression throughput as existing GPU-accelerated
compressors, in terms of compression ratio and quality, cuSZ-I outperforms
other state-of-the-art GPU-based scientific lossy compressors to a significant
extent. It gains compression ratio improvements by up to 500% under the same
error bound or PSNR. In several real-world use cases, cuSZ-I also achieves the
optimized performance, having the minimized time cost for distributed lossy
data transmission tasks and the highest decompression data visualization
quality.
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