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Model‐based MPI‐IO tuning with Periscope tuning framework
Author(s) -
Liu Weifeng,
Gerndt Michael,
Gong Bin
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3603
Subject(s) - computer science , software portability , bottleneck , tuner , parallel computing , supercomputer , code (set theory) , message passing interface , spmd , performance tuning , infiniband , message passing , function (biology) , operating system , embedded system , programming language , telecommunications , radio frequency , evolutionary biology , biology , set (abstract data type)
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.