Automatic and Portable Performance Modeling for Parallel I/O: A Machine-Learning Approach
S.Yu and M. Winslett and J. Lee and X. Ma
In Proceedings of the Workshop on Mathematical Performance Modeling and Analysis, 2002.
Available format:
postscript
Abstract:
A performance model for a parallel I/O system is essential for detailed
performance analyses, automatic performance optimization of I/O request
handling, and potential performance bottleneck identification.
Yet how to build a portable performance model for a parallel I/O system is an
open problem. In this paper, we present a machine-learning approach to
automatic performance modeling for parallel I/O systems. Our approach is
based on the use of a platform-independent performance metamodel, which
is a radial basis function neural network. Given training data, the
metamodel generates a performance model automatically and efficiently for
a parallel I/O system on a given platform.
Preliminary experiments suggest that our goal of having the generated model
provide accurate performance predictions is attainable, for
the parallel I/O library that served as our experimental testbed on an
IBM SP.
This suggests that it is possible to model parallel I/O system performance
automatically and portably, and perhaps to model a broader class of
storage systems as well.