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Publication

Unbalanced Parallel I/O: An Often-Neglected Side Effect of Lossy Scientific Data Compression...

Publication Type
Conference Paper
Book Title
2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7)
Publication Date
Page Numbers
26 to 32
Publisher Location
New Jersey, United States of America
Conference Name
2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7)
Conference Location
St. Louis, Missouri, United States of America
Conference Sponsor
IEEE
Conference Date
-

Lossy compression techniques have demonstrated promising results in significantly reducing the scientific data size while guaranteeing the compression error bounds. However, one important yet often neglected side effect of lossy scientific data compression is its impact on the performance of parallel I/O. Our key observation is that the compressed data size is often highly skewed across processes in lossy scientific compression. To understand this behavior, we conduct extensive experiments where we apply three lossy compressors MGARD, ZFP, and SZ, which are specifically designed and optimized for scientific data, to three real-world scientific applications Gray-Scott simulation, WarpX, and XGC. Our analysis result demonstrates that the size of the compressed data is always skewed even if the original data is evenly decomposed among processes. Such skewness widely exists in different scientific applications using different compressors as long as the information density of the data varies across processes. We then systematically study how this side effect of lossy scientific data compression impacts the performance of parallel I/O. We observe that the skewness in the sizes of the compressed data often leads to I/O imbalance, which can significantly reduce the efficiency of I/O bandwidth utilization if not properly handled. In addition, writing data concurrently to a single shared file through MPI-IO library is more sensitive to the unbalanced I/O loads. Therefore, we believe our research community should pay more attention to the unbalanced parallel I/O caused by lossy scientific data compression.