Developing a parallel Monte Carlo transport algorithm using large-grain data flow

Babb, Robert G.; Storc, Lise; Hiromoto, Robert E.

doi:10.1016/0167-8191(88)90038-5

Cited by 6 publications

(2 citation statements)

References 9 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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“…Pe= fL1 Na (10) A linear log-log fitting was performed using the XCOM database on each side of the absorption edge to compute the 4 parameters required to calculate the photoelectric linear attenuation coefficient which is assumed to have the following form:…”

Section: Petsimmentioning

confidence: 99%

<title>Improvement of the performance and accuracy of PET Monte Carlo simulations</title>

Zaidi

Labbé

Morel

1999

Medical Imaging 1999: Physics of Medical Imaging

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The many applications of Monte Carlo modelling in PET arouse to increase the accuracy and computational speed of Monte Carlo codes. The accuracy of Monte Carlo simulations strongly depends on cross-section libraries used for photon transport calculations. Furthermore, large amounts of CPU time are required to obtain meaningful simulated data. We present a comparison of different photon cross-section libraries together with the parallel implementation of the 3D PET Monte Carlo simulator, Eidolon on a MIMD parallel architecture. Different photon cross-section libraries and parametrizations show quite large variations as compared to the most recent EPDL97 nuclear data files for energies from 1 keV to 1 MeV. Together with the optimisation of the computing time performances of the Monte Carlo software, photon transport in 3D PET could be efficiently modelled to better understand scatter correction techniques. In implementing Eidolon on a parallel platform, a linear speedup factor was achieved with the number of computing nodes.

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