Dennis, J. M.; Jessup, E. R. Applying automated memory analysis to improve iterative algorithms. (English) Zbl 1149.65020 SIAM J. Sci. Comput. 29, No. 5, 2210-2223 (2007). 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. Cited in 1 Document MSC: 65F10 Iterative numerical methods for linear systems 65F50 Computational methods for sparse matrices 68W40 Analysis of algorithms 86A05 Hydrology, hydrography, oceanography Keywords:memory analysis; language processor; sparse linear algebra; ocean modeling; numerical examples Software:SPIRAL; Matlab; Algorithm 679 PDFBibTeX XMLCite \textit{J. M. Dennis} and \textit{E. R. Jessup}, SIAM J. Sci. Comput. 29, No. 5, 2210--2223 (2007; Zbl 1149.65020) Full Text: DOI