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Title: Scalable Replay with Partial-Order Dependencies for Message-Logging Fault Tolerance

Deterministic replay of a parallel application is commonly used for discovering bugs or to recover from a hard fault with message-logging fault tolerance. For message passing programs, a major source of overhead during forward execution is recording the order in which messages are sent and received. During replay, this ordering must be used to deterministically reproduce the execution. Previous work in replay algorithms often makes minimal assumptions about the programming model and application in order to maintain generality. However, in many cases, only a partial order must be recorded due to determinism intrinsic in the code, ordering constraints imposed by the execution model, and events that are commutative (their relative execution order during replay does not need to be reproduced exactly). In this paper, we present a novel algebraic framework for reasoning about the minimum dependencies required to represent the partial order for different concurrent orderings and interleavings. By exploiting this theory, we improve on an existing scalable message-logging fault tolerance scheme. The improved scheme scales to 131,072 cores on an IBM BlueGene/P with up to 2x lower overhead than one that records a total order.
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Conference: IEEE International Conference on Cluster Computing (CLUSTER 2014), September 22-26, 2014, Madrid, Spain, 19-28
IEEE, Piscataway, NJ, United States(US).
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
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Country of Publication:
United States
replay; partial-order dependencies; fault tolerance; message logging