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Resilient Optimistic Termination Detection for the Async-Finish Model

Authors: Sara S. Hamouda; Josh Milthorpe;

Resilient Optimistic Termination Detection for the Async-Finish Model

Abstract

Driven by increasing core count and decreasing mean-time-to-failure in supercomputers, HPC runtime systems must improve support for dynamic task-parallel execution and resilience to failures. The async-finish task model, adapted for distributed systems as the asynchronous partitioned global address space programming model, provides a simple way to decompose a computation into nested task groups, each managed by a ‘finish’ that signals the termination of all tasks within the group.For distributed termination detection, maintaining a consistent view of task state across multiple unreliable processes requires additional book-keeping when creating or completing tasks and finish-scopes. Runtime systems which perform this book-keeping pessimistically, i.e. synchronously with task state changes, add a high communication overhead compared to non-resilient protocols. In this paper, we propose optimistic finish, the first message-optimal resilient termination detection protocol for the async-finish model. By avoiding the communication of certain task and finish events, this protocol allows uncertainty about the global structure of the computation which can be resolved correctly at failure time, thereby reducing the overhead for failure-free execution.Performance results using micro-benchmarks and the LULESH hydrodynamics proxy application show significant reductions in resilience overhead with optimistic finish compared to pessimistic finish. Our optimistic finish protocol is applicable to any task-based runtime system offering automatic termination detection for dynamic graphs of non-migratable tasks.

Keywords

Resilience, Async-finish, Termination detection, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO] Computer Science [cs], [INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
Green