Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912921130369024 |
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| author | Jiao, Pu Di, Sheng Tian, Jiannan Xia, Mingze Wu, Xuan Zhang, Yang Liang, Xin Cappello, Franck |
| author_facet | Jiao, Pu Di, Sheng Tian, Jiannan Xia, Mingze Wu, Xuan Zhang, Yang Liang, Xin Cappello, Franck |
| contents | Error-bounded lossy compression has been regarded as a promising way to address the ever-increasing amount of scientific data in today's high-performance computing systems. Pre-quantization, a critical technique to remove sequential dependency and enable high parallelism, is widely used to design and develop high-throughput error-controlled data compressors. Despite the extremely high throughput of pre-quantization based compressors, they generally suffer from low data quality with medium or large user-specified error bounds. In this paper, we investigate the artifacts generated by pre-quantization based compressors and propose a novel algorithm to mitigate them. Our contributions are fourfold: (1) We carefully characterize the artifacts in pre-quantization based compressors to understand the correlation between the quantization index and compression error; (2) We propose a novel quantization-aware interpolation algorithm to improve the decompressed data; (3) We parallelize our algorithm in both shared-memory and distributed-memory environments to obtain high performance; (4) We evaluate our algorithm and validate it with two leading pre-quantization based compressors using five real-world datasets. Experiments demonstrate that our artifact mitigation algorithm can effectively improve the quality of decompressed data produced by pre-quantization based compressors while maintaining their high compression throughput. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20097 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation Jiao, Pu Di, Sheng Tian, Jiannan Xia, Mingze Wu, Xuan Zhang, Yang Liang, Xin Cappello, Franck Distributed, Parallel, and Cluster Computing Error-bounded lossy compression has been regarded as a promising way to address the ever-increasing amount of scientific data in today's high-performance computing systems. Pre-quantization, a critical technique to remove sequential dependency and enable high parallelism, is widely used to design and develop high-throughput error-controlled data compressors. Despite the extremely high throughput of pre-quantization based compressors, they generally suffer from low data quality with medium or large user-specified error bounds. In this paper, we investigate the artifacts generated by pre-quantization based compressors and propose a novel algorithm to mitigate them. Our contributions are fourfold: (1) We carefully characterize the artifacts in pre-quantization based compressors to understand the correlation between the quantization index and compression error; (2) We propose a novel quantization-aware interpolation algorithm to improve the decompressed data; (3) We parallelize our algorithm in both shared-memory and distributed-memory environments to obtain high performance; (4) We evaluate our algorithm and validate it with two leading pre-quantization based compressors using five real-world datasets. Experiments demonstrate that our artifact mitigation algorithm can effectively improve the quality of decompressed data produced by pre-quantization based compressors while maintaining their high compression throughput. |
| title | Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2602.20097 |