A Hardware Accelerator Based Fast Monte Carlo Code for Radiation Dosimetry: Software Design and Preliminary Results
X Du*, T Liu, L Su, M Riblett, X.G. Xu, Rensselaer Polytechnic Inst., Troy, NYWE-C-108-5 Wednesday 10:30AM - 12:30PM Room: 108
Monte Carlo (MC) simulation provides the most accurate results for CT dose calculations. Yet it is a time-consuming task which often takes long time to run on CPUs. Using the recently developed hardware accelerators such as Graphics Processing Unit (GPU) and the Intel Xeon Phi coprocessor, one can run the MC simulations in massively parallel and reduce the simulation time significantly. In this work we present our effort of developing a new MC suite, Accelerated Radiation-transport Computations in Heterogeneous EnviRonments (ARCHER), which utilizes NVIDIA GPUs and other accelerators.
We first developed the radiation transport MC codes in C, and then adapted the code to ARCHER_GPU and ARCHER_MIC which are specific for NVIDIA GPU and Intel MIC architecture, respectively. Currently the code is capable of calculating the dose for CT X-ray scan and electron radiotherapy. Our code also incorporated a whole CT scanner model and various computational human phantoms.
We tested our GPU-based MC code on NVIDIA Tesla M2090 GPUs and compared its performance with other MC codes running on an Intel Xeon X5650 CPU. Two tests were carried out: 1) For a whole body helical CT scan with RPI-Adult male obese phantom and 9e8 incoming photons, the ARCHER_GPU code and ARCHER_MIC code are 1200 and 561 times faster, respectively, than MCNPX running on the CPU. 2) For a simple water-aluminum-water phantom and 20MeV electron source, the ARCHER_GPU code takes 186 sec to simulate 6e6 electrons, and it is about 1000 times faster than MCNPX running on the CPU.
Our preliminary results showed the efficiency of using different hardware accelerators to speed up MC radiation transport simulations for medical dosimetry. On-going work includes further optimization of the code and implementation of more complex physics models.