As the size and amount of data produced by high-performance computing (HPC) applications grow exponentially, an effective data reduction technique is becoming critical to mitigating time and space burden. Lossy compression techniques, which have been widely used in image and video compression, hold promise to fulfill such data reduction need. However, they are seldom adopted in HPC datasets because of their difficulty in quantifying the amount of information loss and data reduction. In this paper, we explore a lossy compression strategy by revisiting the energy compaction properties of discrete transforms on HPC datasets. Specifically, we apply block-based transforms to HPC datasets, obtain the minimum number of coefficients containing the maximum energy (or information) compaction rate, and quantize remaining non-dominant coefficients using a binning mechanism to minimize information loss expressed in a distortion measure. We implement the proposed approach and evaluate it using six real-world HPC datasets. Our experimental results show that, on average, only 6.67 bits are required to preserve an optimal energy compaction rate on our evaluated datasets. Moreover, our knee detection algorithm improves the distortion in terms of peak signal-to-noise ratio by 2.46 dB on average.
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Characterization of Transform-Based Lossy Compression for HPC Datasets
As the scale and complexity of high-performance computing (HPC) systems keep growing, data compression techniques are often adopted to reduce the data volume and processing time. While lossy compression becomes preferable to a lossless one because of the potential benefit of generating a high compression ratio, it would lose its worth the effort without finding an optimal balance between volume reduction and information loss. Among many lossy compression techniques, transform-based lossy algorithms utilize spatial redundancy better. However, the transform-based lossy compressor has received relatively less attention because there is a lack of understanding of its compression performance on scientific data sets. The insight of this paper is that, in transform-based lossy compressors, quantifying dominant coefficients at the block level reveals the right balance, potentially impacting overall compression ratios. Motivated by this, we characterize three transformation-based lossy compression mechanisms with different information compaction methods using the statistical features that capture data characteristics. And then, we build several prediction models using the statistical features and the characteristics of dominant coefficients and evaluate the effectiveness of each model using six HPC datasets from three production-level simulations at scale. Our results demonstrate that the random forest classifier captures the behavior of dominant coefficients precisely, achieving nearly 99% of prediction accuracy.
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- Award ID(s):
- 1751143
- NSF-PAR ID:
- 10396287
- Date Published:
- Journal Name:
- IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD)
- Page Range / eLocation ID:
- 56 to 62
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DRBSD56682.2022.00013
