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Adaptive runtime support for direct simulation Monte Carlo methods on distributed memory architectures

机译:在分布式内存架构上的直接仿真Monte Carlo方法的自适应运行时支持

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In highly adaptive irregular problems such as many particle-in-cell (PIC) codes and direct simulation Monte Carlo (DSMC) codes, data access patterns may vary from time step to time step. This fluctuation may hinder efficient utilization of distributed memory parallel computers because of the resulting overhead for data redistribution and dynamic load balancing. To efficiently parallelize such adaptive irregular problems on distributed memory parallel computers, several issues such as effective methods for domain partitioning and fast data transportation must be addressed. This paper presents efficient runtime support methods for such problems. A simple one-dimensional domain partitioning method is implemented and compared with unstructured mesh partitioners such as recursive coordinate bisection and recursive inertial bisection. A remapping decision policy has been investigated for dynamic load balancing on 5-dimensional DSMC codes. Performance results are presented.
机译:在高度自适应的不规则问题中,例如许多粒子内(PIC)代码和直接仿真蒙特卡罗(DSMC)代码,数据访问模式可能因时间步长而变化。由于数据再分配和动态负载平衡,因此该波动可能妨碍分布式存储器并行计算机的高效利用率。为了有效地并行化分布式存储器并行计算机上的这种自适应不规则问题,必须解决若干问题,例如用于域分区和快速数据运输的有效方法。本文为这些问题提供了有效的运行时支持方法。实现简单的一维域分区方法,并与非结构化网格分区(例如递归坐标双分和递归惯性二分配)进行比较。已经调查了重复决策策略,用于5维DSMC代码上的动态负载平衡。呈现绩效结果。

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