NanoCheckpoints: A Task-Based Asynchronous Dataflow Framework for Efficient and Scalable Checkpoint/Restart

In this paper, we present NanoCheckpoints which is a lightweight software-based checkpoint/restart scheme for task-parallel HPC applications. We leverage OmpSs, a task-based OpenMP derivative programming model (PM) and its Nanos asynchronous dataflow runtime. NanoCheckpoints achieves minimal overhea...

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Veröffentlicht in:Proceedings - Euromicro Workshop on Parallel and Distributed Processing S. 99 - 102
Hauptverfasser: Subasi, Omer, Arias, Javier, Unsal, Osman, Labarta, Jesus, Cristal, Adrian
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2015
Schlagworte:
Arrays
Benchmark testing
Benchmarks
Checkpoint/restart
Checkpointing
Conferences
Dataflow
Derivatives
Error recovery
Instruction sets
Nanostructure
Networks
Reliability
Runtime
Scalability
Task parallelism
Tasks
Weight reduction
ISSN:1066-6192
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Beschreibung
Zusammenfassung:In this paper, we present NanoCheckpoints which is a lightweight software-based checkpoint/restart scheme for task-parallel HPC applications. We leverage OmpSs, a task-based OpenMP derivative programming model (PM) and its Nanos asynchronous dataflow runtime. NanoCheckpoints achieves minimal overheads by check pointing only tasks' inputs which are available for free in the OmpSs PM. We evaluate NanoCheckpoints by both pure task-parallel shared memory benchmarks (up to 16 cores) and hybrid OmpSs+MPI applications (up to 1024 cores). The results indicate that NanoCheckpoints has on average overhead 3% for shared memory benchmarks. The dataflow semantics of Nanos, where both check pointing and error recovery are asynchronous, allows NanoCheckpoints to scale at large core counts even when high error rates are present. For hybrid OmpSs+MPI benchmarks, NanoCheckpoints has very low overhead, on average 2%, and high scalability.
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SourceType-Conference Papers & Proceedings-2
ISSN:1066-6192
DOI:10.1109/PDP.2015.17