Chapter

Optimizing MPI Runtime Parameter Settings by Using Machine Learning

09/2009; DOI:10.1007/978-3-642-03770-2_26 pp.196-206

ABSTRACT Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific
architecture. However, the best setting for these parameters not only depends on the underlying system but also on the application
itself and its input data. This paper introduces a novel approach based on machine learning techniques to estimate the values
of MPI runtime parameters that tries to achieve optimal speedup for a target architecture and any unseen input program. The
effectiveness of our optimization tool is evaluated against two benchmarks executed on a multi-core SMP machine.

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    Article: Adaptive MPI Multirail Tuning for Non-Uniform Input/Output Access
    Stéphanie Moreaud, Brice Goglin, Raymond Namyst
    [show abstract] [hide abstract]
    ABSTRACT: Multicore processors have not only reintroduced Non-Uniform Memory Access (NUMA) architectures in nowadays parallel computers, but they are also responsible for non-uniform access times with respect to Input/Output devices (NUIOA). In clusters of multicore machines equipped with several Network Interfaces, performance of communication between processes thus depends on which cores these processes are scheduled on, and on their distance to the Network Interface Cards involved. We propose a technique allowing multirail communication between processes to carefully distribute data among the network interfaces so as to counterbalance NUIOA effects. We demonstrate the relevance of our approach by evaluating its implementation within OpenMPI on a Myri-10G + InfiniBand cluster.
    The 17th European MPI Users Group conference.
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Keywords

benchmarks
 
input data
 
Manually tuning MPI runtime parameters
 
MPI runtime parameters
 
multi-core SMP machine
 
optimal speedup
 
optimise MPI application performance
 
paper introduces