MPI+OpenMP混合并行编程模型应用研究

多处理器结点集群在高性能计算市场上日趋流行,如何在多处理器上编写出高效的并行代码成为研究的热点。MPI+OpenMP为多处理器结点集群提供了一种有效的并行策略,结点内部共享内存空间编程模式适合 OpenMP并行,消息传递模型MPI被用在集群的结点与结点之间,这样就实现了并行的层次结构化。     

基于SMP集群系统的并行编程模式研究与分析

并行计算技术是计算机技术发展的重要方向之一,SMP与集群是当前主流的并行体系结构。当前并行程序设计方法主要采用基于消息传递模型的MPI和基于共享存储模型的OpenMP,两种编程模式各有特点和适用范围。对SMP集群以及MPI和OpenMP的特点进行了分析,介绍了在SMP集群系统中利用MPI和OpenMP混合编程的可行性方法。     

基于Docker的MPI和OpenMP混合编程

针对当前搭建集群并行系统复杂且耗时等问题,提出基于Docker搭建并行系统。介绍轻量级虚拟化技术Docker的核心概念和基本架构,并基于Docker技术在Linux平台上搭建集群并行开发环境。简要阐述并行计算的思想,叙述MPI和OpenMP并行计算的基本概念和特点,针对矩阵并行乘法的算法建立MPI和OpenMP的混合编程模型,并给出混合编程模型与MPI并行编程模型以及OpenMP并行编程模型的性能对比,分析出现差异的原因。基于该混合编程模型比较Docker与传统物理机两者搭建的并行系统的并行效率。     

MPI＋OpenMP混合并行编程的分析

简要综述并行计算的思想,并对分布式内存结构和共享式内存结构两种结构的特点进行比较。叙述如何应用OpenMP和MPI进行混合编程,以及相应的混合编程模型。通过一个实例,针对多核CPU组成的SMP构架的集群,实现OpenMP和MPI混合编程的性能对比和结论分析。     

多色SSOR-PCG的MPI+OpenMP混合编程实现

针对对称逐步超松驰预处理共轭梯度（Symmetric Successive Over Relaxation Preconditioned Conjugate Gradient,SSOR-PCG）法并行化时每步迭代都要并行求解2个三角方程组的困难,采用多色排序技术提高并行度,基于MPI＋OpenMP混合编程模型开发适合于分布共享内存计算机的并行程序,通过测试选择有效的MPI通信函数,并给出3种避免共享数据竞争的措施,供不同规模问题和不同内存容量计算机情况选用．     

GRAPES四维变分同化系统MPI和OpenMP混合算法研究

本文阐述了MPI和OpenMP的编程模式,并在此基础上利用MPI和OpenMP混合并行的方式,即:在节点内应用OpenMP共享存储、在节点间应用MPI进行消息传递的模式,对我国自行研发的数值天气预报系统GRAPES(Global/Regional Assimilation and Prediction Syste)进行测试,。结果表明,混合并行算法比原来的单纯的MPI模式有更加理想的并行效率和加速比。     

基于SMP集群的MPI+CUDA模型的研究与实现

为了研究GPU的通用计算能力和适合SMP集群的编程模型,首次提出MPI+CUDA多粒度混合并行编程的新方法,节点间采用MPI实现粗粒度并行,节点内采用CUDA实现细粒度并行的混合编程方式.利用此方法在搭建的3节点SMP集群环境中,测试了大规模矩阵乘问题的并行计算能力.实验结果表明,该方法能够显著提升并行效率,同时证明MPI+CUDA混合编程模型能够充分发挥SMP集群节点间分布式存储和节点内共享内存的优势,为装有CUDA-enabled GPU的SMP集群提供了一种有效的并行策略.     

基于CMP多核集群的混合并行编程技术研究

高性能科学计算(High Performance Science Computing,简称HPC)是验证某些理论和测试计算机系统处理能力的一种有效的实验手段。鉴于目前CMP(Chip Multi-processor)多核集群已变得越来越普及,尝试对由MPI和OpenMP两种不同并行编程技术构成的混合编程模式做一些实验性的研究。通过对程序执行时间和加速比的实验数据分析,可以看出在多核和多节点集群上采用细粒度的混合并行编程方法较单一使用MPI并行编程方法更加合理和高效,也更能体现出系统硬软件的特性与优势。     

基于SMP集群系统的并行编程模式研究与分析

并行计算技术是计算机技术发展的重要方向之一，SMP与集群是当前主流的并行体系结构。当前并行程序设计方法主要采用基于消息传递模型的MPI和基于共享存储模型的OpenMP，两种编程模式各有特点和适用范围。对SMP集群以及MPI和OpenMP的特点进行了分析，介绍了在SMP集群系统中利用MPI和OpenMP混合编程的可行性方法。     

Performance‐based parallel loop self‐scheduling using hybrid OpenMP and MPI programming on multicore SMP clusters

Parallel loop self‐scheduling on parallel and distributed systems has been a critical problem and it is becoming more difficult to deal with in the emerging heterogeneous cluster computing environments. In the past, some self‐scheduling schemes have been proposed as applicable to heterogeneous cluster computing environments. In recent years, multicore computers have been widely included in cluster systems. However, previous researches into parallel loop self‐scheduling did not consider certain aspects of multicore computers; for example, it is more appropriate for shared‐memory multiprocessors to adopt Open Multi‐Processing (OpenMP) for parallel programming. In this paper, we propose a performance‐based approach using hybrid OpenMP and MPI parallel programming, which partition loop iterations according to the performance weighting of multicore nodes in a cluster. Because iterations assigned to one MPI process are processed in parallel by OpenMP threads run by the processor cores in the same computational node, the number of loop iterations allocated to one computational node at each scheduling step depends on the number of processor cores in that node. Experimental results show that the proposed approach performs better than previous schemes. Copyright © 2010 John Wiley & Sons, Ltd.     

The Effect of Process Topology and Load Balancing on Parallel Programming Models for SMP Clusters and Iterative Algorithms

This article focuses on the effect of both process topology and load balancing on various programming models for SMP clusters and iterative algorithms. More specifically, we consider nested loop algorithms with constant flow dependencies, that can be parallelized on SMP clusters with the aid of the tiling transformation. We investigate three parallel programming models, namely a popular message passing monolithic parallel implementation, as well as two hybrid ones, that employ both message passing and multi-threading. We conclude that the selection of an appropriate mapping topology for the mesh of processes has a significant effect on the overall performance, and provide an algorithm for the specification of such an efficient topology according to the iteration space and data dependencies of the algorithm. We also propose static load balancing techniques for the computation distribution between threads, that diminish the disadvantage of the master thread assuming all inter-process communication due to limitations often imposed by the message passing library. Both improvements are implemented as compile-time optimizations and are further experimentally evaluated. An overall comparison of the above parallel programming styles on SMP clusters based on micro-kernel experimental evaluation is further provided, as well.     

基于混合并行的Kriging插值算法研究

普通Kriging方法是进行空间降水插值的一种有效方法。然而一方面由于海量数据插值计算量大,另一方面该算法的时间复杂度大,为减少空间降水插值的计算时间,采用OpenMP和MPI混合并行技术,实现Kriging并行算法。在Windows操作系统上搭建并行计算环境,实验数据表明,该并行算法能有效地节省计算时间。     

基于 MPI ＋OpenMP 的红外弱小目标检测并行计算

为有效监控红外弱小目标运动的全过程,必须采用多个波段同时探测,但是多波段探测必然带来计算时间的大幅增长,无法满足实际应用中对目标检测实时性的要求。针对这一问题,本文提出一种基于MPI＋OpenMP的层次化并行方法,充分利用消息传递模型和共享存储模型的优势,并基于多处理器节点集群进行测试。实验结果表明,该并行程序在保证相同的检测概率的情况下加速比达到8．61,极大地提高了目标检测的效率。     

Performance comparison of MPI and OpenMP on shared memory multiprocessors

When using a shared memory multiprocessor, the programmer faces the issue of selecting the portable programming model which will provide the best performance. Even if they restricts their choice to the standard programming environments (MPI and OpenMP), they have to select a programming approach among MPI and the variety of OpenMP programming styles. To help the programmer in their decision, we compare MPI with three OpenMP programming styles (loop level, loop level with large parallel sections, SPMD) using a subset of the NAS benchmark (CG, MG, FT, LU), two dataset sizes (A and B), and two shared memory multiprocessors (IBM SP3 NightHawk II, SGI Origin 3800). We have developed the first SPMD OpenMP version of the NAS benchmark and gathered other OpenMP versions from independent sources (PBN, SDSC and RWCP). Experimental results demonstrate that OpenMP provides competitive performance compared with MPI for a large set of experimental conditions. Not surprisingly, the two best OpenMP versions are those requiring the strongest programming effort. MPI still provides the best performance under some conditions. We present breakdowns of the execution times and measurements of hardware performance counters to explain the performance differences. Copyright © 2005 John Wiley & Sons, Ltd.     

SMP集群系统上可扩展并行特征问题求解器研究

基于对称三对角特征问题的分而治之方法,提出了一个适合SMP集群环境的多级混合并行算法。SMP节点内的并行求解采用了粗粒度和细粒度两种OpenMP并行。为了改善纯MPI算法中的负载不平衡,混合并行算法使用了动态任务分配方法。在深腾6800上的试验表明,混合并行算法具有好的扩展性和加速比。 关键词：SMP集群;MPI+OpenMP;混合并行;并行求解器     

基于集群技术构建聚变研究高性能计算系统

高性能计算是解决大规模聚变模拟计算的有效途径.介绍如何基于集群技术构建聚变研究高性能计算系统,分析了系统的基本体系结构,从硬件和软件两个方面详细解析其高性价比的构建技术.最后,运用基准测试程序(LinPACK和NPB) 对这一系统进行测试,显示了该系统高效的并行计算性能.