非定常粒子输运蒙特卡罗散射源分层抽样方法(英文)  

A Stratified Sample Method of Scattering Source for Time-dependent Monte Carlo Transport

作　　者：邓力[1] 张文勇[2] 黄正丰[1] 王瑞宏[1] 许海燕[1] 李树[1] 

机构地区：[1]北京应用物理与计算数学研究所计算物理实验室,北京100088 [2]国防科技大学计算机学院,湖南长沙410073

出　　处：《计算物理》2005年第6期527-533,共7页Chinese Journal of Computational Physics

基　　金：supportedbytheunitfoundationofNationalNatureScienceFoundationCommitteeofChinaandChineseAcademyofEngineeringPhysicsandNationalKeyLaboratoryComputationalPhysics

摘　　要：定常粒子输运蒙特卡罗并行计算是成功的,因为粒子游动是独立的,可以把模拟的粒子数等分到每个处理器去.然而,对非定常问题,由于每个时间步涉及散射源和几何网格的通讯,它严重的制约了并行规模,导致并行不可扩展.研究了两种算法,采用自适应分配处理器,提高了加速比和处理器的利用率;采用蒙特卡罗分层抽样大大降低了处理器之间散射源的通讯量,并行可扩展性显著改善,取得了理想的加速比.A parallel algorithm for time-independent Monte Carlo transport is successful since particles are independent and they are distributed to multiple processors. However, for time-dependent Monte Carlo transport problems, the parallel efficiency reduces and the parallel scale is limited due to the communication of scattering source attribute and meshes in each time-step. We propose two algorithms in them adaptive processor assignment and optimized processor choice are obtained. With a Monte Carlo stratified sampling technique for scattering source treatment the communication cost is reduced greatly. The parallel expandability is improved. A large speedup over the basic algorithm is obtained.

关 键 词：非定常 蒙特卡罗输运 自适应处理器分配 散射源分层抽样 

分 类 号：O571.51[理学—粒子物理与原子核物理]