u22Grand Challengeu22 problems such as climate modeling to predict droughts and human genome mapping to predict and possibly cure diseases such as cancer require massive computing power. Three kinds of computer systems currently used in attempts to solve these problems are u22Big Ironu22 multicomputers such as the Intel Paragon, workstation cluster multicomputers, and distributed shared memory multiprocessors such as the Cray T3D. Machines such as these are inefficient in executing some or all of a set of global program operations which are important in many of the u22Grand Challengeu22 programs. These operations include synchronization, reduction, MAX, MIN, one-to-all broadcasting, all-to-all broadcasting, and orderly access to global shared variables. My hypothesis was that a secondary network with a wide tree topology and one or more centralized processors optimized for these operations could substantially decrease their execution time on all three types of systems. To test my hypothesis, I developed the secondary network and Coordination Processor(COP) system described in this dissertation, modeled the major blocks of the design in VHDL, and simulated these blocks to verify their logic and get realistic timing values. The analyses developed for the COP system clearly demonstrate that it can speed up a variety of common global operations by as much as 2-3 orders of magnitude when added to any of several current multicomputers and multiprocessors. Examples show that this speedup reduces overall execution time for important scientific programs and computational kernels by an average of 25% at an increase in system cost of only about 2%. Further analyses show that for these global operations the COP system has a greater combination of speed and versatility than any other system.
展开▼
