Fairness is an important issue when benchmarking parallel computers using application codes. The best parallel algorithm on one platform may not be the best on another. While it is not feasible to re-evaluate parallel algorithms and reimplement large codes whenever new machines become available, it is possible to embed algorithmic options into codes that allow them to be "tuned" for a particular machine without requiring code modifications. We describe a code in which such an approach was taken. PSTSWM was developed for evaluating parallel algorithms for the spectral transform method in atmospheric circulation models. Many levels of runtime-selectable algorithmic options are supported. We discuss these options and our evaluation methodology. We also provide empirical results from a number of parallel machines, indicating the importance of tuning for each platform before making a comparison.
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