Performance Tuning Case Study on Graphics Processing Unit-Accelerated Monte Carlo Simulations for Proton Therapy

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationRACS '19
Subtitle of host publicationProceedings of the Conference on Research in Adaptive and Convergent Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1-6
ISBN (Print)9781450368438
Publication statusPublished - Sep 2019

Publication series

NameProceedings of the Conference on Research in Adaptive and Convergent Systems

Conference

Title2019 Conference on Research in Adaptive and Convergent Systems, RACS 2019
PlaceChina
CityChongqing
Period24 - 27 September 2019

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.

Research Area(s)

  • Autotuning, CUDA, GPU, Monte carlo simulation, Performance, Proton therapy

Citation Format(s)

Performance Tuning Case Study on Graphics Processing Unit-Accelerated Monte Carlo Simulations for Proton Therapy. / Chen, Yi-Shen; Cheng, Sheng-Wei; Kuo, Tei-Wei.

RACS '19: Proceedings of the Conference on Research in Adaptive and Convergent Systems. New York : Association for Computing Machinery, Inc, 2019. p. 1-6 (Proceedings of the Conference on Research in Adaptive and Convergent Systems).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review