The Message Passing Interface (MPI) standard defines virtual topologies that can be applied to systems of cooperating processes. Among issues regarding a more convenient namespace this may be used to optimize the placement of MPI processes in order to reduce communication time. That means, the processes with their main communication paths represent a graph that has to be cost efficiently mapped onto the graph representing the actual communication network. In this context, this work analyses and compares state-of-the-art task mapping strategies with respect to running time and their quality of solutions to the MPI mapping problem. In particular, the focus is on generic strategies that can be used for arbitrary process/network topologies although, here, the topologies of interest are regular ones, where the number of processes is greater than the number of processors in the underlying physical network. Additionally, different measures of mapping quality are discussed and a close correspondence between the most appropriate, the weighted edge cut, and program execution time is shown. In order to investigate how mapping quality affects MPI program execution time, some mapping strategies have been incorporated into Open MPI. Finally, benchmark results prove that optimized process-to-processor mappings can improve program execution time by up to 60%, compared to the default mapping in many MPI implementations (linear mapping). The findings in this work can serve as reference not only for MPI implementors, but also for researchers investigating static process-to-processor mappings, in general.

The Message Passing Interface (MPI) standard defines virtual topologies that can be applied to systems of cooperating processes. Among issues regarding a more convenient namespace this may be used to optimize the placement of MPI processes in order to reduce communication time. That means, the processes with their main communication paths represent a graph that has to be cost efficiently mapped onto the graph representing the actual communication network. In this context, this work analyses and compares state-of-the-art task mapping strategies with respect to running time and their quality of solutions to the MPI mapping problem. In particular, the focus is on generic strategies that can be used for arbitrary process/network topologies although, here, the topologies of interest are regular ones, where the number of processes is greater than the number of processors in the underlying physical network. Additionally, different measures of mapping quality are discussed and a close correspondence between the most appropriate, the weighted edge cut, and program execution time is shown. In order to investigate how mapping quality affects MPI program execution time, some mapping strategies have been incorporated into Open MPI. Finally, benchmark results prove that optimized process-to-processor mappings can improve program execution time by up to 60%, compared to the default mapping in many MPI implementations (linear mapping). The findings in this work can serve as reference not only for MPI implementors, but also for researchers investigating static process-to-processor mappings, in general.