SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors
| العنوان: | SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors |
|---|---|
| المؤلفون: | Kai Zhao, Zizhong Chen, Julie Bessac, Sheng Di, Sihuan Li, Xin Lian, Dingwen Tao, Franck Cappello |
| المصدر: | IEEE BigData |
| بيانات النشر: | IEEE, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Lossless compression, Computer science, business.industry, media_common.quotation_subject, Big data, Data_CODINGANDINFORMATIONTHEORY, Lossy compression, Variety (cybernetics), Computer Science - Distributed, Parallel, and Cluster Computing, Computer engineering, Benchmark (computing), Quality (business), Distributed, Parallel, and Cluster Computing (cs.DC), business, media_common, Data reduction, Volume (compression) |
| الوصف: | Efficient error-controlled lossy compressors are becoming critical to the success of today's large-scale scientific applications because of the ever-increasing volume of data produced by the applications. In the past decade, many lossless and lossy compressors have been developed with distinct design principles for different scientific datasets in largely diverse scientific domains. In order to support researchers and users assessing and comparing compressors in a fair and convenient way, we establish a standard compression assessment benchmark -- Scientific Data Reduction Benchmark (SDRBench). SDRBench contains a vast variety of real-world scientific datasets across different domains, summarizes several critical compression quality evaluation metrics, and integrates many state-of-the-art lossy and lossless compressors. We demonstrate evaluation results using SDRBench and summarize six valuable takeaways that are helpful to the in-depth understanding of lossy compressors. Published in Proceedings of the 1st International Workshop on Big Data Reduction @BigData'20 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fdd9c51566cc70e50f6cf15bd81a24f https://doi.org/10.1109/bigdata50022.2020.9378449 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....6fdd9c51566cc70e50f6cf15bd81a24f |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |