An official website of the United States government
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.
more »
« less
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.
more »
« less
- Award ID(s):
- 1910197
- 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
More Like this
-
-
The likelihood of encountering in-training failures rises substantially with larger Deep Learning (DL) training workloads, leading to lost work and resource wastage. Such failures are typically offset by checkpointing, which comes at the cost of storage and network bandwidth overhead. State-of-the-art approaches involve lossy model compression mechanisms, which induce a tradeoff between the resulting model quality and compression ratio. We make a key enabling observation that the sensitivity of model weights to compression varies during training, and different weights benefit from different quantization levels, ranging from retaining full precision to pruning. We propose (1) a non-uniform quantization scheme that leverages this variation, (2) an efficient search mechanism that dynamically finds the best quantization configurations, and (3) a quantization-aware delta compression mechanism that rearranges weights to minimize checkpoint differences and thereby improving compression. We instantiate these contributions in Inshrinkerator, an in-training checkpoint compression system for DL workloads. Our experiments show that Inshrinkerator consistently achieves a better tradeoff between accuracy and compression ratio compared to prior works, enabling a compression ratio up to 39x and withstanding up to 10 restores with negligible accuracy impact in fault-tolerant training. Inshrinkerator achieves at least an order of magnitude reduction in checkpoint size for failure recovery and transfer learning without any loss of accuracy.more » « less
-
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
-
Valencia, Alfonso (Ed.)Abstract Motivation Nanopore sequencing provides a real-time and portable solution to genomic sequencing, enabling better assembly, structural variant discovery and modified base detection than second generation technologies. The sequencing process generates a huge amount of data in the form of raw signal contained in fast5 files, which must be compressed to enable efficient storage and transfer. Since the raw data is inherently noisy, lossy compression has potential to significantly reduce space requirements without adversely impacting performance of downstream applications. Results We explore the use of lossy compression for nanopore raw data using two state-of-the-art lossy time-series compressors, and evaluate the tradeoff between compressed size and basecalling/consensus accuracy. We test several basecallers and consensus tools on a variety of datasets at varying depths of coverage, and conclude that lossy compression can provide 35–50% further reduction in compressed size of raw data over the state-of-the-art lossless compressor with negligible impact on basecalling accuracy (≲0.2% reduction) and consensus accuracy (≲0.002% reduction). In addition, we evaluate the impact of lossy compression on methylation calling accuracy and observe that this impact is minimal for similar reductions in compressed size, although further evaluation with improved benchmark datasets is required for reaching a definite conclusion. The results suggest the possibility of using lossy compression, potentially on the nanopore sequencing device itself, to achieve significant reductions in storage and transmission costs while preserving the accuracy of downstream applications. Availabilityand implementation The code is available at https://github.com/shubhamchandak94/lossy_compression_evaluation. Supplementary information Supplementary data are available at Bioinformatics online.more » « less
-
With ever-increasing execution scale of the high performance computing (HPC) applications, vast amount of data are being produced by scientific research every day. Error-bounded lossy compression has been considered a very promising solution to address the big-data issue for scientific applications, because it can significantly reduce the data volume with low time cost meanwhile allowing users to control the compression errors with a specified error bound. The existing error-bounded lossy compressors, however, are all developed based on inflexible designs or compression pipelines, which cannot adapt to diverse compression quality requirements/metrics favored by different application users. In this paper, we propose a novel dynamic quality metric oriented error-bounded lossy compression framework, namely QoZ. The detailed contribution is three fold. (1) We design a novel highly-parameterized multi-level interpolation-based data predictor, which can significantly improve the overall compression quality with the same compressed size. (2) We design the error bounded lossy compression framework QoZ based on the adaptive predictor, which can auto-tune the critical parameters and optimize the compression result according to user-specified quality metrics during online compression. (3) We evaluate QoZ carefully by comparing its compression quality with multiple state-of-the-arts on various real-world scientific application datasets. Experiments show that, compared with the second best lossy compressor, QoZ can achieve up to 70% compression ratio improvement under the same error bound, up to 150% compression ratio improvement under the same PSNR, or up to 270% compression ratio improvement under the same SSIM.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DRBSD-549595.2019.00009
