A framework for efficient performance prediction of distributed applications in heterogeneous systems

Predicting distributed application performance is a constant challenge to researchers, with an increased difficulty when heterogeneous systems are involved. Research conducted so far is limited by application type, programming language, or targeted system. The employed models become too complex and prediction cost increases significantly. We propose dPerf, a new performance prediction tool. In dPerf, we extended existing methods from the frameworks Rose and SimGrid. New methods have also been proposed and implemented such that dPerf would perform (i) static code analysis and (ii) trace-based simulation. Based on these two phases, dPerf predicts the performance of C, C++ and Fortran applications communicating using MPI or P2PSAP. Neither one of the used frameworks was developed explicitly for performance prediction, making dPerf a novel tool. dPerf accuracy is validated by a sequential Laplace code and a parallel NAS benchmark. For a low prediction cost and a high gain, dPerf yields accurate results.