Estimating parallel performance

In this paper we introduce our estimation method for parallel execution times, based on identifying separate ''parts'' of the work done by parallel programs. Our run time analysis works without any source code inspection. The time of parallel program execution is expressed in terms of the sequential work and the parallel penalty. We measure these values for different problem sizes and numbers of processors and estimate them for unknown values in both dimensions using statistical methods. This allows us to predict parallel execution time for unknown inputs and non-available processor numbers with high precision. Our prediction methods require orders of magnitude less data points than existing approaches. We verified our approach on parallel machines ranging from a multicore computer to a peta-scale supercomputer. Another useful application of our formalism is a new measure of parallel program quality. We analyse the values for parallel penalty for both growing input size and for increasing numbers of processing elements. From these data, conclusions on parallel performance and scalability are drawn.