HIGH-PERFORMANCE ALGORITHMS AND APPLICATIONS FOR SMP CLUSTERS

The future of high-performance computing relies on the efficient and scalable use of clusters with symmetric multiprocessor (SMP) nodes and low-latency, high-bandwidth interconnection networks. Current examples of such platforms include Sun Ultra HPC machines, Compaq AlphaServers with Quadrics switches, SGI Origins, and the IBM SP system with SMP nodes. Moreover, the future of NASA missioncritical computing for computational aerosciences relies on the success of computational clusters (e.g., SMP Linux clusters at Goddard Space Flight Center, and large SGI Origin arrays at Ames Research Center). Hardware benchmark results reveal awesome performance rates for each component; however, few applications on SMP clusters ever reach a fraction of these peak speeds. While methodologies for symmetric multiprocessors (e.g., OpenMP [21] or POSIX threads [22]) and message-passing primitives for clusters (e.g., MPI [20]) are well developed, performance dictates the use of a hybrid solution. We present preliminary results of our complexity model and programming methodology that is based hierarchically upon realistic model components for message-passing and for symmetric multiprocessor parallel architectures. The current deployment of teraflops and the future development of petaflops systems will certainly require the exploitation of similar hybrid programming models.

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