Hands-on Practical Hybrid Parallel Application Performance Engineering

Geimer, Markus; Gerndt, Michael; Shende, Sameer; Wesarg, Bert; Wylie, Brian J. N.

doi:10.1007/978-3-642-33518-1_6

Cited by 2 publications

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“…This preprocessing inserts monitoring functions into the source code of the application and performs static analysis. The instrumenter also generates a standard intermediate representation file that includes static information such as the instrumented code regions. We will describe source instrumentation when introducing the MPI‐IO plugin.…”

Section: Periscope Tuning Frameworkmentioning

confidence: 99%

Model‐based MPI‐IO tuning with Periscope tuning framework

Liu

Gerndt

Gong

2015

Concurrency and Computation

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Summary For many parallel applications, I/O performance is a major bottleneck. MPI‐IO, defined by the MPI forum, can help parallel applications overcome the performance and portability limitations of existing parallel I/O interfaces. Although autotuning has been used to improve the performance of computing kernels, MPI‐IO autotuning has rarely been studied. To automate MPI‐IO performance tuning, we designed and implemented an automatic tuner. The tuner relies on the Periscope tuning framework for transparently passing hints to the MPI‐IO library and for automatically collecting performance data. Unlike computational code, each MPI‐IO function takes a relatively long time to complete. Thus, exhaustively searching through the entire parameter space is impractical. So we developed a performance model that can direct us to shorten the tuning time. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Periscope Tuning Frameworkmentioning

confidence: 99%