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Accelerated event-by-event Monte Carlo microdosimetric calculations of electrons and protons tracks on a multi-core CPU and a CUDA-enabled GPU

机译:在多核CPU和启用CUDA的GPU上对电子和质子轨迹进行逐事件的蒙特卡洛微剂量加速计算

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摘要

For microdosimetric calculations event-by-event Monte Carlo (MC) methods are considered the most accurate. The main shortcoming of those methods is the extensive requirement for computational time. In this work we present an event-by-event MC code of low projectile energy electron and proton tracks for accelerated microdosimetric MC simulations on a graphic processing unit (GPU). Additionally, a hybrid implementation scheme was realized by employing OpenMP and CUDA in such a way that both GPU and multi-core CPU were utilized simultaneously. The two implementation schemes have been tested and compared with the sequential single threaded MC code on the CPU. Performance comparison was established on the speed-up for a set of benchmarking cases of electron and proton tracks. A maximum speedup of 67.2 was achieved for the GPU-based MC code, while a further improvement of the speedup up to 20% was achieved for the hybrid approach. The results indicate the capability of our CPU-GPU implementation for accelerated MC microdosimetric calculations of both electron and proton tracks without loss of accuracy.
机译:对于微剂量计算,逐事件蒙特卡罗(MC)方法被认为是最准确的。这些方法的主要缺点是对计算时间的广泛需求。在这项工作中,我们展示了低射弹能量电子和质子轨迹的逐事件MC代码,用于图形处理单元(GPU)上的加速微剂量MC模拟。另外,通过以同时利用GPU和多核CPU的方式使用OpenMP和CUDA,实现了一种混合实现方案。这两种实现方案已经过测试,并与CPU上的顺序单线程MC代码进行了比较。性能比较是基于一组电子和质子迹线的基准测试案例的加速情况而建立的。基于GPU的MC代码实现了67.2的最大加速,而混合方法实现了高达20%的加速。结果表明,我们的CPU-GPU实现具有加速电子和质子迹线的MC微剂量计算的能力,而不会降低精度。

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