Performance evaluation and modeling is a crucial process to enable the optimization of parallel programs. Programs written using two programming models, such as MPI and OpenMP, require an analysis to determine both performance efficiency and the most suitable numbers of processes and threads for their execution on a given platform. To study both of these problems, we propose the construction of a model that is based upon a small number of parameters, but is able to capture the complexity of the runtime system. We must incorporate measurements of overheads introduced by each of the programming models, and thus need to model both the network and computational aspects of the system.

We have combined two different techniques: static analysis, driven by the OpenUH compiler, to retrieve application signatures and a parallelization overhead measurement benchmark, realized by Sphinx and Perfsuite, to collect system profiles. Finally, we propose a performance evaluation measurement to identify communication and computation efficiency. In this paper we describe our underlying framework, the performance model, and show how our tool can be applied to a sample code.