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Abstract. A major trend in HPC is the escalation toward manycore, where systems are composed of shared memory nodes featuring numerous processing units. Unfortunately, with scale comes complexity, here in the form of non-uniform memory accesses and cache hierarchies. For most HPC applications, harnessing the power of multicores is hindered by the topology oblivious tuning of the MPI library. In this paper, we propose a framework to tune every type of shared memory communications according to locality and topology. An implementation inside Open MPI is evaluated experimentally and demonstrates significant speedups compared to vanilla Open MPI and MPICH2.

Message Passing Interface (MPI) implementations provide a great flexibility to allow users to arbitrarily bind processes to computing cores to fully exploit clusters of multicore/many-core nodes. An intelligent process placement can optimize application performance according to underlying hardware architecture and the application's communication pattern. However, such static process placement optimization can't help MPI collective communication, whose topology is dynamic with members in each communicator. Conversely, a mismatch between the collective communication topology, the underlying hardware architecture and the process placement often happens due to the MPI's limited capabilities of dealing with complex environments.This paper proposes an adaptive collective communication framework by combining process distance, underlying hardware topologies, and runtime communicator together. Based on this information, an optimal communication topology will be generated to guarantee maximum bandwidth for each MPI collective operation regardless of process placement. Based on this framework, two distance-aware adaptive intra-node collective operations (Broadcast and Allgather) are implemented as examples inside Open MPI's KNEM collective component. The awareness of process distance helps these two operations construct optimal runtime topologies and balance memory accesses across memory nodes. The experiments show these two distance-aware collective operations provide better and more stable performance than current collectives in Open MPI regardless of process placement.

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