Abstract
Lossy compression techniques have demonstrated promising results in significantly reducing the scientific data size while guaranteeing the compression error bounds. However, one important yet often neglected side effect of lossy scientific data compression is its impact on the performance of parallel I/O. Our key observation is that the compressed data size is often highly skewed across processes in lossy scientific compression. To understand this behavior, we conduct extensive experiments where we apply three lossy compressors MGARD, ZFP, and SZ, which are specifically designed and optimized for scientific data, to three real-world scientific applications Gray-Scott simulation, WarpX, and XGC. Our analysis result demonstrates that the size of the compressed data is always skewed even if the original data is evenly decomposed among processes. Such skewness widely exists in different scientific applications using different compressors as long as the information density of the data varies across processes. We then systematically study how this side effect of lossy scientific data compression impacts the performance of parallel I/O. We observe that the skewness in the sizes of the compressed data often leads to I/O imbalance, which can significantly reduce the efficiency of I/O bandwidth utilization if not properly handled. In addition, writing data concurrently to a single shared file through MPI-IO library is more sensitive to the unbalanced I/O loads. Therefore, we believe our research community should pay more attention to the unbalanced parallel I/O caused by lossy scientific data compression.
| Original language | English |
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| Title of host publication | Proceedings of DRBSD-7 2021 |
| Subtitle of host publication | 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 26-32 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781728186726 |
| DOIs | |
| State | Published - 2021 |
| Event | 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-7 2021 - St. Louis, United States Duration: Nov 14 2021 → … |
Publication series
| Name | Proceedings of DRBSD-7 2021: 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis |
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Conference
| Conference | 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-7 2021 |
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| Country/Territory | United States |
| City | St. Louis |
| Period | 11/14/21 → … |
Funding
This work was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration, and National Science Foundation grants CAREER-2048044 and IIS-1838024. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. Furthermore, the research in this project was also supported by the SIRIUS-2 ASCR research project and the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL). We thank the anonymous reviewers for their insightful comments.