Performance evaluation of process partitioning using probabilistic model checking

Consider the problem of partitioning a number of concurrent interacting processes into a smaller number of physical processors. The performance and efficiency of such a system critically depends on the tasks that the processes perform and the partitioning scheme. Although empirical measurements have been extensively used a posteriori to assess the success of partitioning, the results only focus on a subset of possible executions and cannot be generalized. In this paper, we propose a probabilistic state exploration method to evaluate a priori the efficiency of a set of partitions in terms of the speedup they achieve for a given model. Our experiments show that our method is quite effective in identifying partitions that result in better levels of parallelism.