Applying automated memory analysis to improve iterative algorithms
In this paper, we describe automated memory analysis, a technique to improve the memory efficiency of a sparse linear iterative solver. Our automated memory analysis uses a language processor to predict the data movement required for an iterative algorithm based upon a MATLAB implementation. We demonstrate how automated memory analysis is used to reduce the execution time of a component of a global parallel ocean model. In particular, code modifications identified or evaluated through automated memory analysis enable a significant reduction in execution time for the conjugate gradient solver on a small serial problem. Further, we achieve a 9 in total execution time for the full model on 64 processors. The predictive capabilities of our automated memory analysis can be used to simplify the development of memory-efficient numerical algorithms or software.
document
https://n2t.org/ark:/85065/d7zk5gxg
eng
geoscientificInformation
Text
publication
2016-01-01T00:00:00Z
publication
2007-09-28T00:00:00Z
Copyright 2007 Society for Industrial and Applied Mathematics.
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