Tradeoffs in granularity and parallelization for a Monte Carlo shower simulation code

Miura, Kiyotaka; Babb, Robert G.

doi:10.1016/0167-8191(88)90112-3

Cited by 11 publications

(4 citation statements)

References 3 publications

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“…In a review of vectorized Monte Carlo, Martin and Brown 281 described variations of event-based algorithms together with speed-up results published by different groups. During the last two decades, investigations were carried out to run different Monte Carlo codes on multipletransputer systems, 282,283 vector parallel supercomputers, 266,267,284,285 parallel computers 38,286,287 and a cluster of workstations in a local area network using PVM. 288 There are large discrepancies in the performance ratio reported by different authors.…”

Section: ͑I͒mentioning

confidence: 99%

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

1999

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Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography ͑SPECT͒, positron emission tomography ͑PET͒, and multiple emission tomography ͑MET͒ are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. In this paper we present a derivation and methodological basis for this approach and critically review their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed.

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Section: ͑I͒mentioning

confidence: 99%

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

1999

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“…Whilst this does not absolutely guarantee that the number streams for each program are independent from another, it is highly probable, as only a small fraction of the total period of the random number generator is used. We intend implementing a method (Miura and Babb 1987) to guarantee this for certain.…”

Section: Methodsmentioning

confidence: 99%

“…A multiple-processor computer can be used, or more than one separate computer, as long is care is taken that the random numbers used are independent (Press et al 1992), so each processor does not end up simulating the same data. Miura and Babb (1987) discuss how to do this for programs that use multiple processors.…”

Section: Introductionmentioning

confidence: 99%

Parallel operation of Monte Carlo simulations on a diverse network of computers

Kirkby

Delpy²

1997

Phys. Med. Biol.

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Monte Carlo simulation methods are frequently used to determine light propagation in tissue and x-ray propagation as well as for solving other non-medically related problems. Such techniques are computationally slow, with the signal to noise ratio improving only as the square root of computation time. We present a method for the design of a Monte Carlo program that is capable of running on up to 24 computers simultaneously, with there being very few restrictions on the computer types as long as they run on a common network. This parallel operation is useful when the run time is expected to be long. A mixture of PCs and Sun workstations have been successfully used. The program as described was designed for the simulation of light transport in tissue, but the technique of achieving simple simultaneous execution on a number of different computers could be used wherever Monte Carlo techniques are used.

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“…Researchers have expended considerable effort to take advantage of parallel architectures on a variety of codes that fall under the general heading of Monte Carlo trajectory simulations [Martin 1988;Miura and Babb 1988;Frederickson et al 1987], often with very good results. Our initial interest in this work was sparked by knowledge of these efforts and from two additional factors: (1) We were users of a state-of-the-art serial AEM simulation code developed by researchers at the National Institute for Standards and Technology (NIST) [Newbury and Myklebust 1981], but were limited in the size of problems it could tackle, and (2) we have access to parallel supercomputers (nCUBE and Intel machines) that seemed ideal for speeding up these kinds of computations.…”

Section: Introductionmentioning

confidence: 99%

Parallel simulation of electron-solid interactions for electron microscopy modeling

1992

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A parallel implementation of a Monte Carlo algorithm for modeling the scattering of electrons in solids and the resulting X-ray production is described. Two important issues for accurate and fast parallel simulation are discussed--random number generation and load-balancing. Timing results for the parallel simulation are given which show even modest-sized parallel machines can be competitive with conventional vector supercomputers for Monte Carlo trajectory simulations. Examples of parallel calculations performed to analyze specimen composition data and to characterize electron microscope performance are briefly highlighted.

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Tradeoffs in granularity and parallelization for a Monte Carlo shower simulation code

Cited by 11 publications

References 3 publications

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Parallel operation of Monte Carlo simulations on a diverse network of computers

Parallel simulation of electron-solid interactions for electron microscopy modeling

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