Conference on experiences in applying parallel processors to scientific computation

Abstract

There is general agreement that uniprocessor speeds are nearing fundamental limits and that the only way that more speed can be attained is to exploit computational parallelism of one sort or another. The concept is simple but the implementations are not. Clearly, the way to the future must be evolutionary, and it will require the skills of the computer industry, universities, and the national laboratories. The first production mutiprocessor supercomputer systems are now being delivered. Indications are that within the next five years supercomputer manufacturers will offer systems with up to 16 processors. Successful exploitation of their potential performance will require algorithms, software, and hardware that, when combined as a single system, will achieve high-average processor utilization and introduce little additional work relative to a single-processor implementation. The questions of how best to do these things are occupying the attention of researchers all over the world. This work is considered the key to expansion of computer performance. Within the past two years, a significant amount of experimentation on the parallel processing of scientific computation has been done. Most of this work as related to Supercomputers has not been previously reported. Consequently, on March 13-15, Lawrence Livermore National Laboratory and Los Alamos National Laboratory hosted a meeting at Gleneden Beach, Oregon, at which many of these experiments were discussed. The general thrust of these presentations suggests several important results such as 1. A broad spectrum of scientific computation is amenable to parallel processing. The presentations revealed that parallel formulations have been achieved for meaningful computational kernels from such areas as Plasma simulation, Lagrangian fluid flow simulation, Reactor safety simulation, Automated reasoning,