zMesh: Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement
zMesh: Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement
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DOI：
10.1109/ipdps49936.2021.00048
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Huizhang Luo;Junqi Wang;Qing Liu;Jieyang Chen;S. Klasky;N. Podhorszki
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作者：
Huizhang Luo;Junqi Wang;Qing Liu;Jieyang Chen;S. Klasky;N. Podhorszki
高性能计算系统上的科学模拟产生了大量需要有效存储和分析的数据。有损压缩通过牺牲准确性来换取性能，从而显著减少了数据量。尽管ZFP和SZ等有损压缩最近取得了成功，但压缩性能仍然远远不能跟上数据的指数增长。本文旨在进一步利用应用程序的特点，这是一个领域，往往是探索不足，以提高自适应网格细化（AMR）的压缩比-一个广泛使用的数值求解器，允许在有限的区域提高分辨率。我们提出了一个级别重新排序技术zMesh，以减少AMR应用程序的存储空间。特别地，我们将映射到相同或相邻几何坐标的数据点分组，使得数据集更平滑且更可压缩。与压缩性能受元数据开销影响的现有工作不同，这项工作使用链式树结构重新生成恢复配方，因此不涉及压缩数据的额外存储开销，这大大提高了压缩比。结果表明，zMesh可以提高数据的光滑度分别为67.9%和71.3%的Z排序和希尔伯特。总体而言，zMesh将ZFP和SZ的压缩比分别提高了16.5%和133.7%。尽管zMesh涉及额外的计算开销的树和恢复配方建设，我们表明，成本可以摊销的数量被压缩的增加。
Scientific simulations on high-performance computing systems produce vast amounts of data that need to be stored and analyzed efficiently. Lossy compression significantly reduces the data volume by trading accuracy for performance. Despite the recent success of lossy compression, such as ZFP and SZ, the compression performance is still far from being able to keep up with the exponential growth of data. This paper aims to further take advantage of application characteristics, an area that is often under-explored, to improve the compression ratios of adaptive mesh refinement (AMR) - a widely used numerical solver that allows for an improved resolution in limited regions. We propose a level reordering technique zMesh to reduce the storage footprint of AMR applications. In particular, we group the data points that are mapped to the same or adjacent geometric coordinates such that the dataset is smoother and more compressible. Unlike the prior work where the compression performance is affected by the overhead of metadata, this work re-generates restore recipe using a chained tree structure, thus involving no extra storage overhead for compressed data, which substantially improves the compression ratios. The results demonstrate that zMesh can improve the smoothness of data by 67.9% and 71.3% for Z-ordering and Hilbert, respectively. Overall, zMesh improves the compression ratios by up to 16.5% and 133.7% for ZFP and SZ, respectively. Despite that zMesh involves additional compute overhead for tree and restore recipe construction, we show that the cost can be amortized as the number of quantities to be compressed increases.