Parallel 3-D method of characteristics in MPACT
Parallel 3-D method of characteristics in MPACT
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发表时间：
2013-07
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通讯作者：
B. Kochunas;Thomas J. Dovvnar;Zhouyu Liu
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作者：
B. Kochunas;Thomas J. Dovvnar;Zhouyu Liu
在MPACT中开发并实现了一种新的并行三维MOC核，它利用模块化的射线跟踪技术来减少计算量，便于并行分解。该并行模型利用了分布式内存并行和共享内存并行，分别用MPI和OpenMP标准实现。该内核能够并行分解空间、角度以及高达0(104)个处理器的特征射线问题。利用武田3-D传输基准问题对并行3-D MOC核进行了初步验证。MPACT计算的特征值在基准参考的统计不确定度范围内，并与其他参与者的平均值很好地吻合。MPACT k{SUB Eff}与有杆和未杆病例的基准结果分别相差11%和-40%。这些计算是针对不同数量的处理器和多达15625个处理器的并行分解进行的；所有这些计算在收敛时都产生相同的结果。最差情况下的并行效率为60%，而使用500个处理器的情况下观察到非常好的效率(>；95%)。500个处理器的总运行时间为231秒，15625个处理器的总运行时间为19秒。正在进行的工作重点是开发理论性能模型和实现加速技术，以最大限度地减少收敛的迭代次数。(作者)？更少
A new parallel 3-D MOC kernel has been developed and implemented in MPACT which makes use of the modular ray tracing technique to reduce computational requirements and to facilitate parallel decomposition. The parallel model makes use of both distributed and shared memory parallelism which are implemented with the MPI and OpenMP standards, respectively. The kernel is capable of parallel decomposition of problems in space, angle, and by characteristic rays up to 0(104) processors. Initial verification of the parallel 3-D MOC kernel was performed using the Takeda 3-D transport benchmark problems. The eigenvalues computed by MPACT are within the statistical uncertainty of the benchmark reference and agree well with the averages of other participants. The MPACT k{sub eff} differs from the benchmark results for rodded and un-rodded cases by 11 and -40 pcm, respectively. The calculations were performed for various numbers of processors and parallel decompositions up to 15625 processors; all producing the same result at convergence. The parallel efficiency of the worst case was 60%, while very good efficiency (>95%) was observed for cases using 500 processors. The overall run time for the 500 processor case was 231 seconds and 19 seconds for the case with 15625 processors. Ongoing workmore » is focused on developing theoretical performance models and the implementation of acceleration techniques to minimize the number of iterations to converge. (authors)« less