...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computational Physics >Parallel-vector algorithms for particle simulations on shared-memory multiprocessors
【24h】

Parallel-vector algorithms for particle simulations on shared-memory multiprocessors

机译：共享内存多处理器上粒子模拟的并行矢量算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Over the last few decades, the computational demands of massive particle-based simulations for both scientific and industrial purposes have been continuously increasing. Hence, considerable efforts are being made to develop parallel computing techniques on various platforms. In such simulations, particles freely move within a given space, and so on a distributed-memory system, load balancing, i.e., assigning an equal number of particles to each processor, is not guaranteed. However, shared-memory systems achieve better load balancing for particle models, but suffer from the intrinsic drawback of memory access competition, particularly during (1) paring of contact candidates from among neighboring particles and (2) force summation for each particle. Here, novel algorithms are proposed to overcome these two problems. For the first problem, the key is a pre-conditioning process during which particle labels are sorted by a cell label in the domain to which the particles belong. Then, a list of contact candidates is constructed by pairing the sorted particle labels. For the latter problem, a table comprising the list indexes of the contact candidate pairs is created and used to sum the contact forces acting on each particle for all contacts according to Newton's third law. With just these methods, memory access competition is avoided without additional redundant procedures. The parallel efficiency and compatibility of these two algorithms were evaluated in discrete element method (DEM) simulations on four types of shared-memory parallel computers: a multicore multiprocessor computer, scalar supercomputer, vector supercomputer, and graphics processing unit. The computational efficiency of a DEM code was found to be drastically improved with our algorithms on all but the scalar supercomputer. Thus, the developed parallel algorithms are useful on shared-memory parallel computers with sufficient memory bandwidth.
机译：在过去的几十年中，用于科学和工业目的的大规模基于粒子的模拟的计算需求一直在不断增长。因此，为在各种平台上开发并行计算技术付出了巨大的努力。在这种模拟中，粒子在给定的空间内自由移动，因此在分布式内存系统中，不能保证负载平衡，即为每个处理器分配相等数量的粒子。但是，共享内存系统可以为粒子模型实现更好的负载平衡，但是会遭受内存访问竞争的固有缺陷，尤其是在（1）解析相邻粒子之间的候选候选对象和（2）每个粒子的力求和期间。在这里，提出了新颖的算法来克服这两个问题。对于第一个问题，关键是预处理过程，在此过程中，通过细胞所属域中的细胞标记将颗粒标记分类。然后，通过将排序的粒子标签配对来构建候选联系人列表。对于后一个问题，将创建一个包含接触候选对列表索引的表，并根据牛顿第三定律将其用于求和所有接触对每个粒子作用的接触力。仅通过这些方法，就可以避免内存访问竞争，而无需其他冗余过程。在四种类型的共享内存并行计算机上的离散元素方法（DEM）仿真中评估了这两种算法的并行效率和兼容性：多核多处理器计算机，标量超级计算机，矢量超级计算机和图形处理单元。我们发现，除了标量超级计算机之外，我们的算法都极大地提高了DEM代码的计算效率。因此，开发的并行算法可用于具有足够内存带宽的共享内存并行计算机。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号