一个GPU加速的蒙特卡洛代码,RT2用于光子,电子/正子和中子的合运输
Chang-Min Lee1, Sung-Joon Ye1,2,3
1Department of Applied Bioengineering and Research Institute for Convergence Science, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
Physics in medicine and biology
|July 30, 2024
概括
一个新的图形处理单元 (GPU) 加速的蒙特卡洛代码显著加快了用于医疗应用的辐射传输模拟. 这种GPU代码比传统的CPU方法快300倍,有望在常规临床使用.
科学领域:
- 医学物理 医学物理
- 计算科学 计算科学
- 辐射运输运输 辐射运输
背景情况:
- 蒙特卡洛 (MC) 模拟对于医学应用中的辐射传输至关重要.
- 传统的MC代码在CPU架构上经常面临计算瓶.
- 开发高效的GPU加速MC代码对于更快,更准确的模拟是必不可少的.
研究的目的:
- 为配对光子,电子/正子和中子运输开发一个GPU加速的MC代码.
- 为医疗应用相关的广泛能量范围优化代码.
- 为了验证GPU代码的性能和准确性,与已建立的基于CPU的代码进行对比.
主要方法:
- 实现了一个GPU加速的MC代码,使用Nvidia OptiXTM进行射线跟踪硬件加速.
- 优化了内存凝聚和减少分支分歧.
- 用于光子/电子传输的集成EGSnrc模块和用于中子传输的NJOY21数据.
- 使用水和ICRP幻影对基于CPU的FLUKA代码进行验证.
主要成果:
- 与FLUKA相比,GPU代码实现了光子/电子的150-300倍和中子的80-135倍的加速度.
- 在模拟的幻影中,GPU代码和FLUKA之间的剂量差异在2.5%以内.
- 在相关地区,统计不确定性保持在2%以下.
结论:
- 开发的GPU加速MC代码为合辐射传输的计算速度提供了显著的改进.
- 该代码有效地解决了诸如内存访问和GPU架构上的分支分歧等常见挑战.
- 这一进步为MC模拟在放射治疗和诊断中的常规临床实施铺平了道路.
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