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Today’s extreme-scale high-performance computing (HPC) applications are producing volumes of data too large to save or transfer because of limited storage space and I/O bandwidth. Error-bounded lossy compression has been commonly known as one of the best solutions to the big science data issue, because it can significantly reduce the data volume with strictly controlled data distortion based on user requirements. In this work, we develop an adaptive parameter optimization algorithm integrated with a series of optimization strategies for SZ, a state-of-the-art prediction-based compression model. Our contribution is threefold. (1) We exploit effective strategies by using 2nd-order regression and 2nd-order Lorenzo predictors to improve the prediction accuracy significantly for SZ, thus substantially improving the overall compression quality. (2) We design an efficient approach selecting the best-fit parameter setting, by conducting a comprehensive priori compression quality analysis and exploiting an efficient online controlling mechanism. (3) We evaluate the compression quality and performance on a supercomputer with 4,096 cores, as compared with other state-ofthe-art error-bounded lossy compressors. Experiments with multiple real world HPC simulations datasets show that our solution can improve the compression ratio up to 46% compared with the second-best compressor. Moreover, the parallel I/O performance is improved by up to 40% thanks to the significant reduction of data size.
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Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5
Lossy compression is one of the most efficient solutions to reduce storage overhead and improve I/O performance for HPC applications. However, existing parallel I/O libraries cannot fully utilize lossy compression to accelerate parallel write due to the lack of deep understanding on compression-write performance. To this end, we propose to deeply integrate predictive lossy compression with HDF5 to significantly improve the parallel-write performance. Specifically, we propose analytical models to predict the time of compression and parallel write before the actual compression to enable compression-write overlapping. We also introduce an extra space in the process to handle possible data overflows resulting from prediction uncertainty in compression ratios. Moreover, we propose an optimization to reorder the compression tasks to increase the overlapping efficiency. Experiments with up to 4,096 cores from Summit show that our solution improves the write performance by up to 4.5× and 2.9× over the non-compression and lossy compression solutions, respectively, with only 1.5% storage overhead (compared to original data) on two real-world HPC applications.
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- PAR ID:
- 10378007
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- SC22: International Conference for High Performance Computing, Networking, Storage and Analysis
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Scientific simulations generate large amounts of floating-point data, which are often not very compressible using the traditional reduction schemes, such as deduplication or lossless compression. The emergence of lossy floating-point compression holds promise to satisfy the data reduction demand from HPC applications; however, lossy compression has not been widely adopted in science production. We believe a fundamental reason is that there is a lack of understanding of the benefits, pitfalls, and performance of lossy compression on scientific data. In this paper, we conduct a comprehensive study on state-of- the-art lossy compression, including ZFP, SZ, and ISABELA, using real and representative HPC datasets. Our evaluation reveals the complex interplay between compressor design, data features and compression performance. The impact of reduced accuracy on data analytics is also examined through a case study of fusion blob detection, offering domain scientists with the insights of what to expect from fidelity loss. Furthermore, the trial and error approach to understanding compression performance involves substantial compute and storage overhead. To this end, we propose a sampling based estimation method that extrapolates the reduction ratio from data samples, to guide domain scientists to make more informed data reduction decisions.more » « less
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