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Scientific Programming
Volume 21, Issue 3-4, Pages 149-163

McrEngine: A Scalable Checkpointing System Using Data-Aware Aggregation and Compression

Tanzima Zerin Islam,1 Kathryn Mohror,2 Saurabh Bagchi,1 Adam Moody,2 Bronis R. de Supinski,2 and Rudolf Eigenmann1

1School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
2Lawrence Livermore National Laboratory, Livermore, CA, USA

Copyright © 2013 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.