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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Published in:Proceedings - Euromicro Workshop on Parallel and Distributed Processing pp. 99 - 102
Main Authors: Subasi, Omer, Arias, Javier, Unsal, Osman, Labarta, Jesus, Cristal, Adrian
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.03.2015
Subjects:
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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Abstract 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.
AbstractList 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.
Author Labarta, Jesus
Subasi, Omer
Arias, Javier
Unsal, Osman
Cristal, Adrian
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  surname: Unsal
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  surname: Cristal
  fullname: Cristal, Adrian
  email: adrian.cristal@bsc.es
  organization: Barcelona Supercomput. Center, Polytech. Univ. of Catalonia, Barcelona, Spain
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Snippet In this paper, we present NanoCheckpoints which is a lightweight software-based checkpoint/restart scheme for task-parallel HPC applications. We leverage...
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SubjectTerms Arrays
Benchmark testing
Benchmarks
Checkpoint/restart
Checkpointing
Conferences
Dataflow
Derivatives
Error recovery
Instruction sets
Nanostructure
Networks
Reliability
Runtime
Scalability
Task parallelism
Tasks
Weight reduction
Title NanoCheckpoints: A Task-Based Asynchronous Dataflow Framework for Efficient and Scalable Checkpoint/Restart
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