This paper presents two partitioning schemes that guarantee connected components given a connected initial grid.Connected components are import ant for convergence of methods such as domain decomposition or mrdtigrid.For many of the grids tested, the schemes produce partitions as good (in terms of n umber of cut edges) or better than spectral partitioning and require only modest computational resources.This paper describes the two schemes in detail and presents comparison results from a number of t wo and three dimensional unstructured grids.

Abstract We describe the implementation and performance of a three dimensional particle simulation distributed between a Thinking Machines CM‐2 and a Cray Y‐MP. These are connected by a combination of two high‐speed networks: a high‐performance parallel Interface (HIPPI) and an optical network (UltraNct). This is the first application to use this configuration at NASA Ames Research Center. We describe our experience Implementing and using the application and report the results of several timing measurements. We show that the distribution of applications across disparate supcrcomputing platforms Is feasible and has reasonable performance. In addition, several practical aspects of the computing environment arc discussed.

The authors describe the implementation and performance of a three dimensional particle simulation distributed between a Thinking Machines CM-2 and a Cray Y-MP. These are connected by a combination of two high-speed networks; a high performance parallel interface (HIPPI) and an optical network (Ultra Net). This is the first application to use this configuration at NASA Ames Research Center. The authors describe their experience implementing and using the application and report the results of several timing measurements. They show that the distribution of applications across disparate supercomputing platforms is feasible and has reasonable performance. In addition, several practical aspects of the computing environment are discussed. >

The NAS (Numerical Aerodynamic Simulation) parallel benchmarks have been developed at NASA Ames Research Center to study the performance of parallel supercomputer. The eight benchmark problems are specified in a 'pencil and paper' fashion. The performance results of various systems using the NAS parallel benchmarks are presented. These results represent the best results that have been reported to the authors for the specific systems listed. They represent implementation efforts performed by personnel in both the NAS Applied Research Branch of NASA Ames Research Center and in other organizations.>

Abstract Sorting on a parallel architecture is a communications intensive event which can incur a high penalty in applications where it is required. In the case of particle simulation, only integer sorting is necessary, and sequential implementations easily attain the minimum performance bound of O(N) for N particles. Parallel implementations, however, have to cope with the parallel sorting problem which, in addition to incurring a heavy communications cost, can make the minimum performance bound difficult to attain. This paper demonstrates how the sorting problem in a particle simulation can be reduced to a merging problem, and describes an efficient data parallel algorithm to solve this merging problem in a particle simulation. The new algorithm is shown to be optimal under conditions usual for particle simulation, and its fieldwise implementation on the Connection Machine is analysed in detail. The new algorithm is about four times faster than a fieldwise implementation of radix sort on the Connection Machine.

Article Free Access Share on The NAS parallel benchmarks—summary and preliminary results Authors: D. H. Bailey Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , E. Barszcz Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , J. T. Barton Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , D. S. Browning Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , R. L. Carter Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , L. Dagum Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , R. A. Fatoohi Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , P. O. Frederickson Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , T. A. Lasinski Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , R. S. Schreiber Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , H. D. Simon Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , V. Venkatakrishnan Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile , S. K. Weeratunga Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CA Numerical Aerodynamic Simulation (NAS) Systems Division, NASA Ames Research Center, Mail Stop T045-1, Moffett Field, CAView Profile Authors Info & Claims Supercomputing '91: Proceedings of the 1991 ACM/IEEE conference on SupercomputingAugust 1991 Pages 158–165https://doi.org/10.1145/125826.125925Published:01 August 1991Publication History 405citation1,162DownloadsMetricsTotal Citations405Total Downloads1,162Last 12 Months161Last 6 weeks25 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A very efficient direct particle simulation algorithm for hypersonic rarefied flows is presented and its implementation on a Connection Machine is described. The implementation is capable of simulating up to 4 x 106 hard sphere diatomic molecules using 64k processors with a performance better than that of a similar, fully vectorized implementation using a single processor of the Cray 2. Results from flow calculations are presented to demonstrate both the validity of the implementation and the range afforded by the method in solving hypersonic rarefied flow problems. Finally, a breakdown of the calculation time is given to identify a bottleneck which must be resolved for further improvement in performance.