The first massively parallel processors (MPP) became available in the late 1980s, with considerable expectations brought about by their impressive peak performance. However, it soon became evident that the sustained or actual performance of these machines could be significantly less than peak because of the time required for interprocessor and related communication. In an effort to improve performance, parallel communication algorithms were developed, which in theory, demonstrated that actual performance could be substantially improved, to the extent that it met or even exceeded that of traditional multiprocessors. However, this was not possible at that time because the MPP architectures did not have the features necessary to provide optimal implementation of these algorithms. That is, wallclock time could not match theoretical algorithmic performance without certain architectural features that were not available in the marketplace. These features have been incorporated into a recently patented parallel computer called the Vector Multiprocessor (VMP). It is the product of an effort to determine the optimum algorithmic and architectural environment for weather/climate models. It is a novel, general-purpose, high-performance, scalable multiprocessor that evolved from the answers to such questions as: How do we minimize communication and maximize performance? Will communication ultimately dominate multiprocessor performance? Is there a "best" multiprocessor architecture? In addition to the usual complement of logic and arithmetic units, each processor contains a programmable communication unit. Interprocessor communication tasks are performed to and from local vector registers in the same way that computational tasks are performed on a vector uniprocessor. The VMP brings to the multiprocessor what vectorization brought to the single processor. Here we determine optimum multiprocessor performance for the key computational kernels used in spectral models of climate and global dynamics, and in doing so, define the VMP.

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