Abstract
The parallelization of Electro-Magnetic Cascade Monte Carlo Simulation Code, EGS4 on distributed memory scalar parallel computer: Intel Paragon XP/S15-256 is described. EGS4 has the feature that calculation time for one incident particle is quite different from each other because of the dynamic generation of secondary particles and different behavior of each particle. Granularity for parallel processing, parallel programming model and the algorithm of parallel random number generation are discussed and two kinds of method, each of which allocates particles dynamically or statically, are used for the purpose of realizing high speed parallel processing of this code. Among four problems chosen for performance evaluation, the speedup factors for three problems have been attained to nearly 100 times with 128 processor. It has been found that when both the calculation time for each incident particles and its dispersion are large, it is preferable to use dynamic particle allocation method which can average the load for each processor. And it has also been found that when they are small, it is preferable to use static particle allocation method which reduces the communication overhead. Moreover, it is pointed out that to get the result accurately, it is necessary to use double precision variables in
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Citation Formats
Takemiya, Hiroshi, Ohta, Hirofumi, and Honma, Ichirou.
The parallel processing of EGS4 code on distributed memory scalar parallel computer:Intel Paragon XP/S15-256.
Japan: N. p.,
1996.
Web.
Takemiya, Hiroshi, Ohta, Hirofumi, & Honma, Ichirou.
The parallel processing of EGS4 code on distributed memory scalar parallel computer:Intel Paragon XP/S15-256.
Japan.
Takemiya, Hiroshi, Ohta, Hirofumi, and Honma, Ichirou.
1996.
"The parallel processing of EGS4 code on distributed memory scalar parallel computer:Intel Paragon XP/S15-256."
Japan.
@misc{etde_368207,
title = {The parallel processing of EGS4 code on distributed memory scalar parallel computer:Intel Paragon XP/S15-256}
author = {Takemiya, Hiroshi, Ohta, Hirofumi, and Honma, Ichirou}
abstractNote = {The parallelization of Electro-Magnetic Cascade Monte Carlo Simulation Code, EGS4 on distributed memory scalar parallel computer: Intel Paragon XP/S15-256 is described. EGS4 has the feature that calculation time for one incident particle is quite different from each other because of the dynamic generation of secondary particles and different behavior of each particle. Granularity for parallel processing, parallel programming model and the algorithm of parallel random number generation are discussed and two kinds of method, each of which allocates particles dynamically or statically, are used for the purpose of realizing high speed parallel processing of this code. Among four problems chosen for performance evaluation, the speedup factors for three problems have been attained to nearly 100 times with 128 processor. It has been found that when both the calculation time for each incident particles and its dispersion are large, it is preferable to use dynamic particle allocation method which can average the load for each processor. And it has also been found that when they are small, it is preferable to use static particle allocation method which reduces the communication overhead. Moreover, it is pointed out that to get the result accurately, it is necessary to use double precision variables in EGS4 code. Finally, the workflow of program parallelization is analyzed and tools for program parallelization through the experience of the EGS4 parallelization are discussed. (author).}
place = {Japan}
year = {1996}
month = {Mar}
}
title = {The parallel processing of EGS4 code on distributed memory scalar parallel computer:Intel Paragon XP/S15-256}
author = {Takemiya, Hiroshi, Ohta, Hirofumi, and Honma, Ichirou}
abstractNote = {The parallelization of Electro-Magnetic Cascade Monte Carlo Simulation Code, EGS4 on distributed memory scalar parallel computer: Intel Paragon XP/S15-256 is described. EGS4 has the feature that calculation time for one incident particle is quite different from each other because of the dynamic generation of secondary particles and different behavior of each particle. Granularity for parallel processing, parallel programming model and the algorithm of parallel random number generation are discussed and two kinds of method, each of which allocates particles dynamically or statically, are used for the purpose of realizing high speed parallel processing of this code. Among four problems chosen for performance evaluation, the speedup factors for three problems have been attained to nearly 100 times with 128 processor. It has been found that when both the calculation time for each incident particles and its dispersion are large, it is preferable to use dynamic particle allocation method which can average the load for each processor. And it has also been found that when they are small, it is preferable to use static particle allocation method which reduces the communication overhead. Moreover, it is pointed out that to get the result accurately, it is necessary to use double precision variables in EGS4 code. Finally, the workflow of program parallelization is analyzed and tools for program parallelization through the experience of the EGS4 parallelization are discussed. (author).}
place = {Japan}
year = {1996}
month = {Mar}
}