Abstract
This paper presents the ANDES performance evaluation tool. ANDES is based on the synthetic execution of parallel programs and it is used for the evaluation of mapping strategies. The Meganode, a distributed memory parallel computer, is considered as our target architecture. ANDES takes into account a benchmark of quantitative models of parallel algorithms and a set of mapping strategies (greedy and iterative algorithms are used). We show how this tool allows an extensive comparison of mapping strategies by using the benchmark, the mapping strategies and different cost functions.
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