Abstract-The increasing numbers of cores, shared caches and memory nodes within machines introduces a complex hardware topology. High-performance computing applications now have to carefully adapt their placement and behavior according to the underlying hierarchy of hardware resources and their software affinities.We introduce the Hardware Locality (hwloc) software which gathers hardware information about processors, caches, memory nodes and more, and exposes it to applications and runtime systems in a abstracted and portable hierarchical manner. hwloc may significantly help performance by having runtime systems place their tasks or adapt their communication strategies depending on hardware affinities.We show that hwloc can already be used by popular highperformance OPENMP or MPI software. Indeed, scheduling OPENMP threads according to their affinities or placing MPI processes according to their communication patterns shows interesting performance improvement thanks to hwloc. An optimized MPI communication strategy may also be dynamically chosen according to the location of the communicating processes in the machine and its hardware characteristics.
The emergence of multicore processors raises the need to efficiently transfer large amounts of data between local processes. MPICH2 is a highly portable MPI implementation whose large-message communication schemes suffer from high CPU utilization and cache pollution because of the use of a double-buffering strategy, common to many MPI implementations. We introduce two strategies offering a kernel-assisted, single-copy model with support for noncontiguous and asynchronous transfers. The first one uses the now widely available vmsplice Linux system call; the second one further improves performance thanks to a custom kernel module called KNEM. The latter also offers I/OAT copy offload, which is dynamically enabled depending on both hardware cache characteristics and message size. These new solutions outperform the standard transfer method in the MPICH2 implementation when no cache is shared between the processing cores or when very large messages are being transferred. Collective communication operations show a dramatic improvement, and the IS NAS parallel benchmark shows a 25% speedup and better cache efficiency.
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