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GPU‐accelerated Monte Carlo convolution/superposition implementation for dose calculation
Author(s) -
Zhou Bo,
Yu Cedric X.,
Chen Danny Z.,
Hu X. Sharon
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3490083
Subject(s) - computer science , monte carlo method , speedup , graphics processing unit , convolution (computer science) , superposition principle , computational science , central processing unit , range (aeronautics) , graphics , parallel computing , cuda , software , algorithm , computer hardware , artificial intelligence , computer graphics (images) , mathematics , artificial neural network , mathematical analysis , statistics , materials science , composite material , programming language
Purpose: Dose calculation is a key component in radiation treatment planning systems. Its performance and accuracy are crucial to the quality of treatment plans as emerging advanced radiation therapy technologies are exerting ever tighter constraints on dose calculation. A common practice is to choose either a deterministic method such as the convolution/superposition (CS) method for speed or a Monte Carlo (MC) method for accuracy. The goal of this work is to boost the performance of a hybrid Monte Carlo convolution/superposition (MCCS) method by devising a graphics processing unit (GPU) implementation so as to make the method practical for day‐to‐day usage. Methods: Although the MCCS algorithm combines the merits of MC fluence generation and CS fluence transport, it is still not fast enough to be used as a day‐to‐day planning tool. To alleviate the speed issue of MC algorithms, the authors adopted MCCS as their target method and implemented a GPU‐based version. In order to fully utilize the GPU computing power, the MCCS algorithm is modified to match the GPU hardware architecture. The performance of the authors' GPU‐based implementation on an Nvidia GTX260 card is compared to a multithreaded software implementation on a quad‐core system. Results: A speedup in the range of 6.7–11.4× is observed for the clinical cases used. The less than 2% statistical fluctuation also indicates that the accuracy of the authors' GPU‐based implementation is in good agreement with the results from the quad‐core CPU implementation. Conclusions: This work shows that GPU is a feasible and cost‐efficient solution compared to other alternatives such as using cluster machines or field‐programmable gate arrays for satisfying the increasing demands on computation speed and accuracy of dose calculation. But there are also inherent limitations of using GPU for accelerating MC‐type applications, which are also analyzed in detail in this article.