As the amount of data produced by HPC applications reaches the exabyte range, compression techniques are often adopted to reduce the checkpoint time and volume. Since lossless techniques are limited in their ability to achieve appreciable data reduction, lossy compression becomes a preferable option. In this work, a lossy compression technique with highly efficient encoding, purpose-built error control, and high compression ratios is proposed. Specifically, we apply a discrete cosine transform with a novel block decomposition strategy directly to double-precision floating point datasets instead of prevailing prediction-based techniques. Further, we design an adaptive quantization with two specific task-oriented quantizers: guaranteed error bounds and higher compression ratios. Using real-world HPC datasets, our approach achieves 3x-38x compression ratios while guaranteeing specified error bounds, showing comparable performance with state-of-the-art lossy compression methods, SZ and ZFP. Moreover, our method provides viable reconstructed data for various checkpoint/restart scenarios in the FLASH application, thus is considered to be a promising approach for lossy data compression in HPC I/O software stacks.
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Analyzing the Performance and Accuracy of Lossy Checkpointing on Sub-Iteration of NWChem
Future exascale systems are expected to be characterized by more frequent failures than current petascale systems. This places increased importance on the application to minimize the amount of time wasted due to recompution when recovering from a checkpoint. Typically HPC application checkpoint at iteration boundaries. However, for applications that have a high per-iteration cost, checkpointing inside the iteration limits the amount of re-computation. This paper analyzes the performance and accuracy of using lossy compressed check-pointing in the computational chemistry application NWChem. Our results indicate that lossy compression is an effective tool for reducing the sub-iteration checkpoint size. Moreover, compression error tolerances that yield acceptable deviation in accuracy and iteration count are quantified.
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- Award ID(s):
- 1910197
- NSF-PAR ID:
- 10193342
- Date Published:
- Journal Name:
- 10.1109/DRBSD-549595.2019.00009
- Page Range / eLocation ID:
- 23 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models. In this paper, we propose a lossy compression scheme for checkpoint constructions (called LC-Checkpoint). LC-Checkpoint simultaneously maximizes the compression rate and optimizes the recovery speed, under the assumption that SGD is used to train the model. LC-Checkpointuses quantization and priority promotion to store the most crucial information for SGD to recover, and then uses a Huffman coding to leverage the non-uniform distribution of the gradient scales. Our extensive experiments show that LC-Checkpoint achieves a compression rate up to 28× and recovery speedup up to 5.77× over a state-of-the-art algorithm (SCAR).more » « less
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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.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DRBSD-549595.2019.00009
