Doppler broadening using discrete cosine transform and kernel reconstruction for spatially variable media

Liu, Changyuan

doi:10.1016/j.anucene.2022.109150

Cited by 2 publications

(5 citation statements)

References 61 publications

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“…The high fidelity simulation requires handling with continuously variable temperatures. In earlier researches, the on-the-fly Doppler broadening method named 'kernel reconstruction' is demonstrated to be effective [17], and suitable for implementing on GPUs, and work well with continuously variable materials [18]. Therefore, the test code adopts the kernel reconstruction method.…”

Section: B Reference Codementioning

confidence: 99%

Monte Carlo neutron transport using low power mobile GPU devices

Liu¹

2022

Preprint

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Abstract-The using of GPU for Monte Carlo particle transport is lacking of fair comparisons. This work performs simulations on both CPU and GPU in the same package under the same manufacturing process of low power mobile devices. The experiment with simple pincell benchmark problems with fresh fuel gives consistent results between CPU and GPU. In the meanwhile, it finds that the Apple M1 GPU is as twice capable as M1 CPU, while entitled with a 5 times advantage in power consumption. The particle sorting algorithm optimized for GPU improves computing efficiency by 28%, while prominently reducing GPU power consumption. Such advantage of sorting algorithm is expected to be greater for depleted fuel problems than fresh fuel problem. The kernel reconstruction Doppler broadening algorithm designed for continuously varying materials is demonstrated to produce consistent Doppler coefficients with the reference code and the algorithm can be efficiently implemented on GPU. Compared with the reference code with double precision floating point numbers, the testing codes with single precision floating point numbers could underestimate the K-effective values by about 500 pcm, and the Doppler coefficients of the fuel are well reproduced though. The conclusion may strengthen the argument that it is helpful for high performance computer to adopt GPU in order to reduce gross power consumption. This work is an English translated version of an article submitted to the CORPHY 2022 conference(http://corphy2022.org.cn). Original text is in Chinese, and there may be minor difference in the contents. This work is also an extension to the published article. https://www.sciencedirect.com/science/article/pii/S0306454922001852

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Section: B Reference Codementioning

confidence: 99%

Monte Carlo neutron transport using low power mobile GPU devices

Liu¹

2022

Preprint

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“…The high fidelity reactor simulation requires handling with continuously variable temperatures. In earlier researches, the on-the-fly Doppler broadening method named 'kernel reconstruction' is demonstrated to be effective [17], and suitable for implementing on GPUs, and work well with continuously variable materials [18]. Therefore, the test code adopts the kernel reconstruction method.…”

Section: B Reference Codementioning

confidence: 99%

“…This work is also an extension to the published article [18]. Computing technologies from BigCompute Laboratory are used to produce parts of the data in this article.…”

Section: Acknowledgmentmentioning

confidence: 99%

“…The cross sections are prepared in an optimal set of 13 temperatures for the kernel reconstruction Doppler broadening method, which is suitable for neutron transport in continuously variable media [9]. For OpenMC, cross sections at the same set of temperatures are used, and the 2-point interpolation Doppler broadening method is used.…”

Section: A Simulation Configurationmentioning

confidence: 99%

“…This collaboration is previously uneconomic because of the low data transfer rate and high data transfer latency between CPUs and GPUs on computing devices with separated CPUs and GPUs. There are previous work study the Apple silicon for Monte Carlo methods in areas such as: CPU performance study [9], multicore CPU work balance [10], and cross section lookup on GPU [11].…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo neutron transport using low power mobile GPU devices

Liu

2022

Preprint

Self Cite

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Doppler broadening using discrete cosine transform and kernel reconstruction for spatially variable media

Cited by 2 publications

References 61 publications

Monte Carlo neutron transport using low power mobile GPU devices

Monte Carlo neutron transport using low power mobile GPU devices

Monte Carlo neutron transport using low power mobile GPU devices

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