Monte Carlo Photon Transport On Shared Memory and Distributed Memory Parallel Processors

Parallelized Monte Carlo algorithms for analyzing photon transport in an inertially confined fusion (ICF) plasma are consid ered. Algorithms were developed for shared memory (vector and scalar) and distributed memory (scalar) parallel pro cessors. The shared memory algorithm was implemented on the IBM 3090/400, and timing results are presented for dedi cated runs with two, three, and four pro cessors. Two alternative distributed memory algorithms (replication and dis patching) were implemented on a hyper cube parallel processor (1 through 64 nodes). The replication algorithm yields essentially full efficiency for all cube sizes; with the 64-node configuration, the absolute performance is nearly the same as with the CRAY X-MP The dispatching algorithm also yields efficiencies above 80% in a large simulation for the 64-pro cessor configuration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67146/2/10.1177_109434208700100306.pd

Parallell Slump

Slumptalsgenerering er eit av dei mest fundamentale og klassiske problemai informatikk. Der er ei lang rekkje kjende gode slumptalsgeneratorarfor sekvensielle program. Parallell programmering stiller nye krav tilslumptalsgeneratorane, og ulike parallelliseringsparadigme har ulike behov.Når me krev at programmet skal vera deterministisk utelukker me mange avdei vanlegaste løysingane. Trass i at parallellisering av slumptalsgeneratorarhar vore kjend som ei utfordring i 30 år, er der få generelle løysingar iliteraturen. I denne artikkelen gjev me eit overblikk over kjende løysingar ogviser korleis splittbare slumptalsgeneratorar kan brukast til ein deterministisk,dataparellell implementasjon av genetiske algoritmar

Probabilistic structural mechanics research for parallel processing computers

Aerospace structures and spacecraft are a complex assemblage of structural components that are subjected to a variety of complex, cyclic, and transient loading conditions. Significant modeling uncertainties are present in these structures, in addition to the inherent randomness of material properties and loads. To properly account for these uncertainties in evaluating and assessing the reliability of these components and structures, probabilistic structural mechanics (PSM) procedures must be used. Much research has focused on basic theory development and the development of approximate analytic solution methods in random vibrations and structural reliability. Practical application of PSM methods was hampered by their computationally intense nature. Solution of PSM problems requires repeated analyses of structures that are often large, and exhibit nonlinear and/or dynamic response behavior. These methods are all inherently parallel and ideally suited to implementation on parallel processing computers. New hardware architectures and innovative control software and solution methodologies are needed to make solution of large scale PSM problems practical

Monte Carlo methods for radiation transport analysis on vector computers

The development of advanced computers with special capabilities for vectorized or parallel calculations demands the development of new calculational methods. The very nature of the Monte Carlo process precludes direct conversion of old (scalar) codes to the new machines. Instead, major changes in global algorithms and careful selection of compatible physics treatments are required. Recent results for Monte Carlo in multigroup shielding applications and in continuous-energy reactor lattice analysis have demonstrated that Monte Carlo methods can be successfully vectorized. The significant effort required for stylized coding and major algorithmic changes is worthwhile, and significant gains in computational efficiency are realized. Speedups of at least twenty to forty times faster than CDC-7600 scalar calculations have been achieved on the CYBER-205 without sacrificing the accuracy of standard Monte Carlo methods. Speedups of this magnitude provide reductions in statistical uncertainties for a given amount of computing time, permit more detailed and realistic problems to be analyzed, and make the Monte Carlo method more accessible to nuclear analysts. Following overviews of the Monte Carlo method for particle transport analysis and of vector computer hardware and software characteristics, both general and specific aspects of the vectorization of Monte Carlo are discussed. Finally, numerical results obtained from vectorized Monte Carlo codes run on the CYBER-205 are presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24996/1/0000423.pd