Network performance-aware collective communication for clustered wide-area systems
Metacomputing infrastructures couple multiple clusters (or MPPs) via wide-area networks. A major problem in programming parallel applications for such platforms is their hierarchical network structure: latency and bandwidth of WANs often are orders of magnitude worse than those of local networks. Our goal is to optimize MPI's collective operations for such platforms. We use two techniques: selecting suitable communication graph shapes, and splitting messages into multiple segments that are sent in parallel over different WAN links. To optimize graph shape and segment size at runtime, we introduce a performance model called Parameterized LogP , a hierarchical extension of the LogP model that covers messages of arbitrary length. An experimental performance evaluation shows that the newly implemented collective operations have significantly improved performance for large messages, and that there is a close match between the theoretical model and the measured completion times.
collective communication for clustered wide-area systems mpi grid computing