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PRISM: An Efficient GPU-Based Lossy Compression Framework for Progressive Data Retrieval with Multi-Level Interpolation
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DOI：10.1145/3774934.3786438.png)
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Abstract
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With the exponential growth of computing power, large-scale scientific simulations are producing massive volumes of data, leading to critical storage and I/O challenges. Error-bounded lossy compression has become one of the most effective solutions for reducing data size while preserving accuracy. Leveraging GPU is increasingly essential for high-performance compression on such large datasets, but GPU-based lossy compressors usually support only single-precision decompression, limiting their ability to meet the diverse accuracy requirements of scientific workflows. Progressive compressors can address this limitation by enabling on-demand precision retrieval. However, existing progressive lossy compressors on GPU still suffer from low throughput. To overcome these challenges, we present PRISM, a GPU-based progressive lossy compressor that achieves both high throughput and multi-precision retrieval, which introduces a high performance progressive framework that integrates the multiple interpolation predictors, efficient bitplane extraction, and an enhanced lossless compression that combines sign-absolute coding with zero-aware parallel algorithms. Evaluations on representative real-world datasets from five scientific domains show that PRISM significantly outperforms state-of-the-art progressive compressors on GPU, reducing retrieval data volume by over 15.6x and achieving up to 20.1x higher throughput on the NVIDIA H100 GPU under the same error bounds.
Keywords:
Lossy compressor
Progressive decompression
High performance
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Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets
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