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Parallelization of a Monte Carlo particle transport simulation code

机译:蒙特卡洛粒子传输模拟代码的并行化

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We have developed a high performance version of the Monte Carlo particle transport simulation code MC4. The original application code, developed in Visual Basic for Applications (VBA) for Microsoft Excel, was first rewritten in the C programming language for improving code portability. Several pseudo-random number generators have been also integrated and studied. The new MC4 version was then parallelized for shared and distributed-memory multiprocessor systems using the Message Passing Interface. Two parallel pseudo-random number generator libraries (SPRNG and DCMT) have been seamlessly integrated. The performance speedup of parallel MC4 has been studied on a variety of parallel computing architectures including an Intel Xeon server with 4 dual-core processors, a Sun cluster consisting of 16 nodes of 2 dual-core AMD Opteron processors and a 200 dual-processor HP cluster. For large problem size, which is limited only by the physical memory of the multiprocessor server, the speedup results are almost linear on all systems. We have validated the parallel implementation against the serial VBA and C implementations using the same random number generator. Our experimental results on the transport and energy loss of electrons in a water medium show that the serial and parallel codes are equivalent in accuracy. The present improvements allow for studying of higher particle energies with the use of more accurate physical models, and improve statistics as more particles tracks can be simulated in low response time.
机译:我们已经开发了蒙特卡洛粒子传输模拟代码MC4的高性能版本。最初的应用程序代码是在Visual Basic for Microsoft Excel的应用程序(VBA)中开发的,最初是用C编程语言重写的,以提高代码的可移植性。几个伪随机数生成器也已被集成和研究。然后,使用消息传递接口将新的MC4版本并行化,以用于共享和分布式内存多处理器系统。两个并行的伪随机数生成器库(SPRNG和DCMT)已无缝集成。并行MC4的性能提速已经在各种并行计算架构上进行了研究,包括具有4个双核处理器的Intel Xeon服务器,由2个双核AMD Opteron处理器的16个节点组成的Sun集群和200个双处理器HP簇。对于较大的问题(仅受多处理器服务器的物理内存限制),加速结果在所有系统上几乎都是线性的。我们已经使用相同的随机数生成器针对串行VBA和C实现对并行实现进行了验证。我们在水介质中电子的传输和能量损失的实验结果表明,串行和并行编码的精度相同。当前的改进允许通过使用更精确的物理模型来研究更高的粒子能量,并且由于可以在低响应时间内模拟更多的粒子轨迹而改善了统计量。

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