Toward General Software Level Silent Data Corruption Detection for Parallel Applications
Silent data corruption (SDC) poses a great challenge for high-performance computing (HPC) applications as we move to extreme-scale systems. Mechanisms have been proposed that are able to detect SDC in HPC applications by using the peculiarities of the data (more specifically, its “smoothness” in time and space) to make predictions. However, these data-analytic solutions are still far from fully protecting applications to a level comparable with more expensive solutions such as full replication. In this work, we propose partial replication to overcome this limitation. More specifically, we have observed that not all processes of an MPI application experience the same level of data variability at exactly the same time. Thus, we can smartly choose and replicate only those processes for which the lightweight data-analytic detectors would perform poorly. In addition, we propose a new evaluation method based on the probability that a corruption will pass unnoticed by a particular detector (instead of just reporting overall single-bit precision and recall). Here in our experiments, we use four applications dealing with different explosions. Finally, our results indicate that our new approach can protect the MPI applications analyzed with 7–70% less overhead (depending on the application) than that of full duplication with similar detection recall.
- Research Organization:
- Bettis Atomic Power Lab. (BAPL), West Mifflin, PA (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); Institut national de recherche en informatique et en automatique (INRIA); Agence Nationale de la recherche (ANR); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1413980
- Journal Information:
- IEEE Transactions on Parallel and Distributed Systems, Vol. 28, Issue 12; ISSN 1045-9219
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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