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Applying automated memory analysis to improve iterative algorithms. (English) Zbl 1149.65020

Summary: We describe an 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 the 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.

MSC:

65F10 Iterative numerical methods for linear systems
65F50 Computational methods for sparse matrices
68W40 Analysis of algorithms
86A05 Hydrology, hydrography, oceanography
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