Kernel-assisted and Topology-aware MPI Collective Communication among Multicore or Many-core Clusters

Multicore or many-core clusters have become the most prominent form of High Performance Computing (HPC) systems. Hardware complexity and hierarchies not only exist in the inter-node layer, i.e., hierarchical networks, but also exist in internals of multicore compute nodes, e.g., Non Uniform Memory Accesses (NUMA), networkstyle interconnect, and memory and shared cache hierarchies. Message Passing Interface (MPI), the most widely adopted in the HPC communities, suffers from decreased performance and portability due to increased hardware complexity of multiple levels. We identified three critical issues specific to collective communication: The first problem arises from the gap between logical collective topologies and underlying hardware topologies; Second, current MPI communications lack efficient shared memory message delivering approaches; Last, on distributed memory machines, like multicore clusters, a single approach cannot encompass the extreme variations not only in the bandwidth and latency capabilities, but also in features such as the aptitude to operate multiple concurrent copies simultaneously. To bridge the gap between logical collective topologies and hardware topologies, we developed a distance-aware framework to integrate the knowledge of hardware distance into collective algorithms in order to dynamically reshape the communication patterns to suit the hardware capabilities. Based on process distance information, we used graph partitioning techniques to organize the MPI processes in a multilevel hierarchy, mapping on the hardware characteristics. Meanwhile, we took advantage of the kernel-assisted one-sided single-copy approach (KNEM) as the