Performance prediction using an application-oriented mapping tool

Simulation frameworks are widely used to carry out performance predictions of parallel programs. In general, these environments do not support the use of automatic mapping mechanisms for assigning tasks to processors. We present a tool called pMAP (predicting the best mapping of parallel applications) that performs the mapping process of message-passing applications starting from the characterization of application behaviour. We show that it is important to explore in these simulation frameworks, not only how many resources are needed to achieve good results, but also how best to map the application onto the parallel system. We study the evaluation of the pMAP tool integrated with the commercial simulator DIMEMAS. The results show that for message-passing applications with different task structures, great improvements in the speedup can be obtained when simulations are carried out with the mappings generated by pMAP.