Hardware Acceleration of an Efficient and Accurate Proton Therapy Monte Carlo

Osiecki, T.; Tsai, Ming-Rung; Gattiker, A.; Jamsek, Damir A.; Nassif, Sani R.; Speight, William Evan; Sze, Cliff

doi:10.1016/j.procs.2013.05.395

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…8,9 To the best of our knowledge, such an optimization has not been clinically feasible due to the long calculation times associated with traditional MC dose computations. As demonstrated previously, [14][15][16][17] graphics processing units (GPUs) can be used to accelerate proton transport MC with impressive results. Recently, a GPU-based proton transport MC capable of processing 10 7 proton histories in less than 30 s on one GPU card has been developed at our institution 8,9 and extensively validated using Geant4.…”

Section: Introductionmentioning

confidence: 86%

A GPU‐accelerated and Monte Carlo‐based intensity modulated proton therapy optimization system

Beltran

Tseung

et al. 2014

Medical Physics

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Section: Introductionmentioning

confidence: 86%

A GPU‐accelerated and Monte Carlo‐based intensity modulated proton therapy optimization system

Beltran

Tseung

et al. 2014

Medical Physics

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“…One can also find research on the use of customized FFTs for asynchronous execution and mapping FFT based Poisson solvers to multi node systems [9,10,11]. Numerous studies [12,13,14,15,16,17] have been carried out to show the potential of GPUs and Intel Xeon Phi co-processors for Monte-Carlo simulations for proton and photon transport. These problems are some of the most time consuming parts of the OPAL simulations, and previous research shows that they are good candidates for acceleration on the co-processors.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Kernel Scheduler—Part 1

Adelmann

Locans

Suter

2016

Computer Physics Communications

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Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software that uses these hardware accelerators introduces additional challenges for the developer. These challenges may include exposing increased parallelism, handling different hardware designs, and using multiple development frameworks in order to utilise devices from different vendors.The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between the host application and different hardware accelerators. DKS handles the communication between the host and the device, schedules task execution, and provides a library of built-in algorithms. Algorithms available in the DKS library will be written in CUDA, OpenCL, and OpenMP. Depending on the available hardware, the DKS can select the appropriate implementation of the algorithm.The first DKS version was created using CUDA for the Nvidia GPUs and OpenMP for Intel MIC. DKS was further integrated into OPAL (Object-oriented Parallel Accelerator Library) in order to speed up a parallel FFT based Poisson solver and Monte Carlo simulations for particle matter interaction used for proton therapy degrader modelling. DKS was also used together with Minuit2 for parameter fitting, where χ 2 and max-log-likelihood functions were offloaded to the hardware accelerator. The concepts of the DKS, first results, and plans for the future will be shown in this paper.

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A fast GPU-based Monte Carlo simulation of proton transport with detailed modeling of nonelastic interactions

2015

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Our GPU-based MC is the first of its kind to include a detailed nuclear model to handle nonelastic interactions of protons with any nucleus. Dosimetric calculations are in very good agreement with (GEANT)4.9.6p2/TOPAS. Our MC is being integrated into a framework to perform fast routine clinical QA of pencil-beam based treatment plans, and is being used as the dose calculation engine in a clinically applicable MC-based IMPT treatment planning system. The detailed nuclear modeling will allow us to perform very fast linear energy transfer and neutron dose estimates on the GPU.

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Hardware Acceleration of an Efficient and Accurate Proton Therapy Monte Carlo

Cited by 5 publications

References 15 publications

A GPU‐accelerated and Monte Carlo‐based intensity modulated proton therapy optimization system

A GPU‐accelerated and Monte Carlo‐based intensity modulated proton therapy optimization system

The Dynamic Kernel Scheduler—Part 1

A fast GPU-based Monte Carlo simulation of proton transport with detailed modeling of nonelastic interactions

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