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A Communication Optimization Scheme for Basis Computation of Krylov Subspace Methods on Multi-GPUs Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications

Authors : Shahzeb Siddiqui, Fatemah AlZayer, Saber Feki

Published in: High Performance Computing for Computational Science -- VECPAR 2014

Publisher: Springer International Publishing

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Abstract

The performance optimization of scientific applications usually requires an in-depth knowledge of the hardware and software. A performance tuning mechanism is suggested to automatically tune OpenACC parameters to adapt to the execution environment on a given system. A historic learning based methodology is suggested to prune the parameter search space for a more efficient auto-tuning process. This approach is applied to tune the OpenACC gang and vector clauses for a better mapping of the compute kernels onto the underlying architecture. Our experiments show a significant performance improvement against the default compiler parameters and drastic reduction in tuning time compared to a brute force search-based approach.

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previous chapter Environment-Sensitive Performance Tuning for Distributed Service Orchestration
next chapter Capturing the Expert: Generating Fast Matrix-Multiply Kernels with Spiral
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Metadata
Title
Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications
Authors
Shahzeb Siddiqui
Fatemah AlZayer
Saber Feki
Copyright Year
2015
Book
High Performance Computing for Computational Science -- VECPAR 2014

Print ISBN: 978-3-319-17352-8

Electronic ISBN: 978-3-319-17353-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-17353-5_19

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