FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913201819484160 |
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| author | Chen, Xinyu Tian, Jiannan Beaver, Ian Freeman, Cynthia Yan, Yan Wang, Jianguo Tao, Dingwen |
| author_facet | Chen, Xinyu Tian, Jiannan Beaver, Ian Freeman, Cynthia Yan, Yan Wang, Jianguo Tao, Dingwen |
| contents | While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With the HPC community increasingly leaning towards in-situ analysis and visualization, more floating-point data from scientific simulations are being stored in databases like Key-Value Stores and queried using in-memory retrieval paradigms. This trend underscores the urgent need for a collective study of these compression methods' strengths and limitations, not only based on their performance in compressing data from various domains but also on their runtime characteristics. Our study extensively evaluates the performance of eight CPU-based and five GPU-based compression methods developed by both communities, using 33 real-world datasets assembled in the Floating-point Compressor Benchmark (FCBench). Additionally, we utilize the roofline model to profile their runtime bottlenecks. Our goal is to offer insights into these compression methods that could assist researchers in selecting existing methods or developing new ones for integrated database and HPC applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10301 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data Chen, Xinyu Tian, Jiannan Beaver, Ian Freeman, Cynthia Yan, Yan Wang, Jianguo Tao, Dingwen Databases While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With the HPC community increasingly leaning towards in-situ analysis and visualization, more floating-point data from scientific simulations are being stored in databases like Key-Value Stores and queried using in-memory retrieval paradigms. This trend underscores the urgent need for a collective study of these compression methods' strengths and limitations, not only based on their performance in compressing data from various domains but also on their runtime characteristics. Our study extensively evaluates the performance of eight CPU-based and five GPU-based compression methods developed by both communities, using 33 real-world datasets assembled in the Floating-point Compressor Benchmark (FCBench). Additionally, we utilize the roofline model to profile their runtime bottlenecks. Our goal is to offer insights into these compression methods that could assist researchers in selecting existing methods or developing new ones for integrated database and HPC applications. |
| title | FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data |
| topic | Databases |
| url | https://arxiv.org/abs/2312.10301 |