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Predictive communication modeling for HPC applications
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DOI：10.1007/s10586-017-0821-8.png)
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Abstract
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In this paper, we present a methodology for predictive modeling of communication of HPC applications. Communication time depends on a complex set of parameters, relevant to the application, the system architecture, the runtime configuration and runtime conditions. To handle this complexity, we define features that can be extracted from the application, the process mapping and the allocation shape ahead of execution, deploy a single benchmark to sweep over the parameter space and develop predictive models for communication time on two supercomputers, Vilje and Piz Daint, using different subsets of our features, machine-learning methods and training sets. We compare the predictive power of our models on two common communication patterns and one application, for various problem sizes, executions and runtime configurations, ranging from a few dozen to a few thousand cores. Our methodology is successful across all tested communication patterns on both systems and exhibits high prediction accuracy and goodness-of-fit, scoring 23.98% in MMRE, 0.942 in RCC and 61.43% in Pred(0.25) on Vilje and 21.31%, 0.940 and 66.57% respectively on Piz Daint, with models that are applicable just-in-time ahead of the execution of an HPC application.
Keywords:
Predictive modeling
Communication time
MPI applications
Supercomputers
Machine learning
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