Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1137/060661533 | SIAM J. Scientific Computing |
Keywords | Field | DocType |
improve iterative algorithms,execution time,full model,conjugate gradient solver,global parallel ocean model,automated memory analysis,matlab implementation,iterative algorithm,total execution time,sparse linear iterative solver,memory analysis,memory efficiency,conjugate gradient,floating point | Conjugate gradient method,MATLAB,Iterative method,Computer science,Parallel computing,Algorithm,Software,Memory analysis,Execution time,Solver,Numerical analysis | Journal |
Volume | Issue | ISSN |
29 | 5 | 1064-8275 |
Citations | PageRank | References |
6 | 0.50 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. M. Dennis | 1 | 41 | 2.75 |
| E. R. Jessup | 2 | 100 | 11.48 |