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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Compression of Time Evolutionary Image Data through Predictive Deep Neural Networks
Recent advances in Deep Neural Networks (DNNs) have demonstrated a promising potential in predicting the temporal and spatial proximity of time evolutionary data. In this paper, we have developed an effective (de)compression framework called TEZIP that can support dynamic lossy and lossless compression of time evolutionary image frames with high compression ratio and speed. TEZIP first trains a Recurrent Neural Network called PredNet to predict future image frames based on base frames, and then derives the resulting differences between the predicted frames and the actual frames as more compressible delta frames. Next we equip TEZIP with techniques that can exploit spatial locality for the encoding of delta frames and apply lossless compressors on the resulting frames. Furthermore, we introduce window-based prediction algorithms and dynamically pinpoint the trade-off between the window size and the relative errors of predicted frames. Finally, we have conducted an extensive set of tests to evaluate TEZIP. Our experimental results show that, in terms of compression ratio, TEZIP outperforms existing lossless compressors such as x265 by up to 3.2x and lossy compressors such as SZ by up to 3.3x.
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- NSF-PAR ID:
- 10228579
- Date Published:
- Journal Name:
- The 21st IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely on trial and error to assess lossy compression performance. As a strong data-driven effort toward quantifying lossy compressibility of scientific datasets, we provide a statistical framework to predict compression ratios of lossy compressors. Our method is a two-step framework where (i) compressor-agnostic predictors are computed and (ii) statistical prediction models relying on these predictors are trained on observed compression ratios. Proposed predictors exploit spatial correlations and notions of entropy and lossyness via the quantized entropy. We study 8+ compressors on 6 scientific datasets and achieve a median percentage prediction error less than 12%, which is substantially smaller than that of other methods while achieving at least a 8.8× speedup for searching for a specific compression ratio and 7.8× speedup for determining the best compressor out of a collection.
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Scientific simulations run by high-performance computing (HPC) systems produce a large amount of data, which causes an extreme I/O bottleneck and a huge storage burden. Applying compression techniques can mitigate such overheads through reducing the data size. Unlike traditional lossless compressions, error-controlled lossy compressions, such as SZ, ZFP, and DCTZ, designed for scientists who demand not only high compression ratios but also a guarantee of certain degree of precision, is coming into prominence. While rate-distortion efficiency of recent lossy compressors, especially the DCT-based one, is promising due to its high-compression encoding, the overall coding architecture is still conservative, necessitating the quantization that strikes a balance between different encoding possibilities and varying rate-distortions. In this paper, we aim to improve the performance of DCT-based compressor, namely DCTZ, by optimizing the quantization model and encoding mechanism. Specifically, we propose a bit-efficient quantizer based on the DCTZ framework, develop a unique ordering mechanism based on the quantization table, and extend the encoding index. We evaluate the performance of our optimized DCTZ in terms of rate-distortion using real-world HPC datasets. Our experimental evaluations demonstrate that, on average, our proposed approach can improve the compression ratio of the original DCTZ by 1.38x. Moreover, combined with the extended encoding mechanism, the optimized DCTZ shows a competitive performance with state-of-the-art lossy compressors, SZ and ZFP.more » « less
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High-performance computing (HPC) systems that run scientific simulations of significance produce a large amount of data during runtime. Transferring or storing such big datasets causes a severe I/O bottleneck and a considerable storage burden. Applying compression techniques, particularly lossy compressors, can reduce the size of the data and mitigate such overheads. Unlike lossless compression algorithms, error-controlled lossy compressors could significantly reduce the data size while respecting the user-defined error bound. DCTZ is one of the transform-based lossy compressors with a highly efficient encoding and purpose-built error control mechanism that accomplishes high compression ratios with high data fidelity. However, since DCTZ quantizes the DCT coefficients in the frequency domain, it may only partially control the relative error bound defined by the user. In this paper, we aim to improve the compression quality of DCTZ. Specifically, we propose a preconditioning method based on level offsetting and scaling to control the magnitude of input of the DCTZ framework, thereby enforcing stricter error bounds. We evaluate the performance of our method in terms of compression ratio and rate distortion with real-world HPC datasets. Our experimental result shows that our method can achieve a higher compression ratio than other state-of-the-art lossy compressors with a tighter error bound while precisely guaranteeing the user-defined error bound.more » « less
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As parallel computers continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design for an efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ), which is the first lossy compressor that can achieve high compression ratios and throughputs simultaneously. Specifically, we propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline, from a variety of representative scientific datasets. Moreover, we derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also helps us create a fixed-ratio compression mode for consistent throughput. In addition, we develop an efficient compression pipeline by adopting cuSZ's dual-quantization algorithm to our hardware use cases. Finally, we evaluate CEAZ on five real-world datasets with both a single FPGA board and 128 nodes (to accelerate parallel I/O). Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2X of throughput and 9.6X of ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 28.1X and 36.9X, respectively.more » « less
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Accepted Manuscript1.0
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