Performance tuning case study on graphics processing unit-accelerated monte carlo simulations for proton therapy.
Journal
Proceedings of the 2019 Research in Adaptive and Convergent Systems, RACS 2019
Pages
1-6
Date Issued
2019
Author(s)
Abstract
Proton radiation therapy is one of the most effective modern methods of cancer treatment. Because radiation cannot distinguish between tumors and healthy tissue, simulating beams to predict the range of radiation is crucial. Monte Carlo simulation is the most accurate dose calculation method for radiotherapy. However, high accuracy requires extremely long computation time, thus limiting its clinical applications. To date, considerable efforts have been made to utilize graphics processing unit (GPU)-accelerated algorithm designs for accelerating the proton dose simulation. However, to completely utilize the capability of a specific GPU, the GPU configurations must be fine-tuned carefully. In this study, we propose the performance evaluation of GPUaccelerated Monte Carlo simulation programs for proton therapy. Considerable efforts have been made to properly use an autotuning tool to determine optimal configurations for the aforementioned programs. Furthermore, the source of counterintuitive observations was examined. Evaluation results show that appropriate configurations can considerably reduce the simulation time of programs. ? 2019 Association for Computing Machinery.
Subjects
Autotuning; CUDA; GPU; Monte carlo simulation; Performance; Proton therapy
SDGs
Other Subjects
Computer graphics; Computer graphics equipment; Intelligent systems; Monte Carlo methods; Program processors; Proton beam therapy; Proton beams; Radiotherapy; Autotuning; Clinical application; CUDA; Evaluation results; Performance; Performance tuning; Proton radiation therapy; Proton therapy; Graphics processing unit
Type
conference paper