文章基本信息

标题：McrEngine: A Scalable Checkpointing System Using Data-Aware Aggregation and Compression
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作者：Tanzima Zerin Islam, Kathryn Mohror, Saurabh Bagchi, Adam Moody, Bronis R. de Supinski ; Rudolf Eigenmann
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2013
卷号：21
DOI：10.3233/SPR-130371
出版社：Hindawi Publishing Corporation
摘要：High performance computing (HPC) systems use checkpoint-restart to tolerate failures. Typically, applications store their states in checkpoints on a parallel file system (PFS). As applications scale up, checkpoint-restart incurs high overheads due to contention for PFS resources. The high overheads force large-scale applications to reduce checkpoint frequency, which means more compute time is lost in the event of failure. We alleviate this problem through a scalable checkpoint-restart system, mcrEngine. McrEngine aggregates checkpoints from multiple application processes with knowledge of the data semantics available through widely-used I/O libraries, e.g., HDF5 and netCDF, and compresses them. Our novel scheme improves compressibility of checkpoints up to 115% over simple concatenation and compression. Our evaluation with large-scale application checkpoints show that mcrEngine reduces checkpointing overhead by up to 87% and restart overhead by up to 62% over a baseline with no aggregation or compression.