Identifying Latent Reduced Models to Precondition Lossy Compression
Identifying Latent Reduced Models to Precondition Lossy Compression
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DOI：
10.1109/ipdps.2019.00039
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Huizhang Luo;Dan Huang;Qing Liu;Zhenbo Qiao;Hong Jiang;J. Bi;Haitao Yuan;Mengchu Zhou;Jinzhen Wang;Zhenlu Qin
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文献类型：
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作者：
Huizhang Luo;Dan Huang;Qing Liu;Zhenbo Qiao;Hong Jiang;J. Bi;Haitao Yuan;Mengchu Zhou;Jinzhen Wang;Zhenlu Qin
随着高性能计算系统产生的科学数据量巨大且增长迅速，提升压缩性能变得愈发关键。利用应用程序对精度降低的普遍容忍度，有损压缩器能够在用户设定的误差范围内实现高得多的压缩比。然而，它们仍远不能满足应用程序对数据量缩减的要求。在本文中，我们提出并评估了一种观点，即数据在压缩之前需要进行预处理，以便能更好地契合压缩器的设计理念。具体而言，我们旨在找出一个降维模型，用于将原始数据转换为更易于压缩的形式。我们首先以Heat3d案例研究作为概念验证，展示了降维模型确实可以存在于完整模型的输出中，并且能够用于提高压缩比。我们进一步探索了更通用的降维技术来提取降维模型，包括主成分分析、奇异值分解和离散小波变换。预处理后，存储降维模型与增量数据，从而实现更高的压缩比。我们在九个科学数据集上对降维模型进行了评估，结果表明了我们方法的有效性。
With the high volume and velocity of scientific data produced on high-performance computing systems, it has become increasingly critical to improve the compression performance. Leveraging the general tolerance of reduced accuracy in applications, lossy compressors can achieve much higher compression ratios with a user-prescribed error bound. However, they are still far from satisfying the reduction requirements from applications. In this paper, we propose and evaluate the idea that data need to be preconditioned prior to compression, such that they can better match the design philosophies of a compressor. In particular, we aim to identify a reduced model that can be utilized to transform the original data to a more compressible form. We begin with a case study of Heat3d as a proof of concept, in which we demonstrate that a reduced model can indeed reside in the full model output, and can be utilized to improve compression ratios. We further explore more general dimension reduction techniques to extract the reduced model, including principal component analysis, singular value decomposition, and discrete wavelet transform. After preconditioning, the reduced model in conjunction with delta is stored, which results in higher compression ratios. We evaluate the reduced models on nine scientific datasets, and the results show the effectiveness of our approaches.