Study on Parallel Computation and Load Balance Strategy Based on Multiblock Structured Grid

从并行计算流体力学程序的稳定性和效率两大问题入手,针对多块结构网格的通用数据传输方法和基于遗传优化算法的负载平衡方法,并在已有串行多块结构网格程序基础上发展了相应的并行程序。该并行程序以物理区域分割为基础,采用MPI实现消息传递,适用于各种不同的并行机体系结构,具有很好的可移植性。大量数值实验证明,本文发展的并行程序具有良好的稳定性和并行效率,可以进一步应用于大规模实际工程计算

Study on Parallel Computation and Load Balance Strategy Based on Multiblock Structured Grid

从并行计算流体力学程序的稳定性和效率两大问题入手,针对多块结构网格的通用数据传输方法和基于遗传优化算法的负载平衡方法,并在已有串行多块结构网格程序基础上发展了相应的并行程序。该并行程序以物理区域分割为基础,采用MPI实现消息传递,适用于各种不同的并行机体系结构,具有很好的可移植性。大量数值实验证明,本文发展的并行程序具有良好的稳定性和并行效率,可以进一步应用于大规模实际工程计算

A partitioner-centric model for SAMR partitioning trade-off optimization : Part II.

Optimal partitioning of structured adaptive mesh applications necessitates dynamically determining and optimizing for the most time-inhibiting factor, such as data migration and communication volume. However, a trivial monitoring of an application evaluates the current partitioning rather than the inherent properties of the grid hierarchy. We present a model that given a structured adaptive grid, determines ab initio to what extent the partitioner should focus on reducing the amount of data migration to reduce execution time. This model contributes to the meta-partitioner, our ultimate aim of being able to select and configure the optimal partitioner based on the dynamic properties of the grid hierarchy and the computer. We validate the predictions of this model by comparing them with actual measurements (via traces) from four different adaptive simulations. The results show that the proposed model generally captures the inherent optimization-need in SAMR applications. We conclude that our model is a useful contribution, since tracking and adapting to the dynamic behavior of such applications lead to potentially large decreases in execution times

A heuristic re-mapping algorithm reducing inter-level communication in SAMR applications.

This paper aims at decreasing execution time for large-scale structured adaptive mesh refinement (SAMR) applications by proposing a new heuristic re-mapping algorithm and experimentally showing its effectiveness in reducing inter-level communication. Tests were done for five different SAMR applications. The overall goal is to engineer a dynamically adaptive meta-partitioner capable of selecting and configuring the most appropriate partitioning strategy at run-time based on current system and application state. Such a metapartitioner can significantly reduce execution times for general SAMR applications. Computer simulations of physical phenomena are becoming increasingly popular as they constitute an important complement to real-life testing. In many cases, such simulations are based on solving partial differential equations by numerical methods. Adaptive methods are crucial to efficiently utilize computer resources such as memory and CPU. But even with adaption, the simulations are computationally demanding and yield huge data sets. Thus parallelization and the efficient partitioning of data become issues of utmost importance. Adaption causes the workload to change dynamically, calling for dynamic (re-) partitioning to maintain efficient resource utilization. The proposed heuristic algorithm reduced inter-level communication substantially. Since the complexity of the proposed algorithm is low, this decrease comes at a relatively low cost. As a consequence, we draw the conclusion that the proposed re-mapping algorithm would be useful to lower overall execution times for many large SAMR applications. Due to its usefulness and its parameterization, the proposed algorithm would constitute a natural and important component of the meta-partitioner