Hybrid CUDA, OpenMP, and MPI parallel programming on multicore GPU clusters

Nowadays, NVIDIA's CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. In this paper, we propose a parallel programming approach using hybrid CUDA OpenMP, and MPI programming, which partition loop iterations according to the number of C1060 GPU nodes in a GPU cluster which consists of one C1060 and one S1070. Loop iterations assigned to one MPI process are processed in parallel by CUDA run by the processor cores in the same computational node.     

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。     

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.     

Scalability and performance of OpenMP and MPI on a 128‐processor SGI Origin 2000

The purpose of this paper is to investigate the scalability and performance of seven, simple OpenMP test programs and to compare their performance with equivalent MPI programs on an SGI Origin 2000. Data distribution directives were used to make sure that the OpenMP implementation had the same data distribution as the MPI implementation. For the matrix‐times‐vector (test 5) and the matrix‐times‐matrix (test 7) tests, the syntax allowed in OpenMP 1.1 does not allow OpenMP compilers to be able to generate efficient code since the reduction clause is not currently allowed for arrays. (This problem is corrected in OpenMP 2.0.) For the remaining five tests, the OpenMP version performed and scaled significantly better than the corresponding MPI implementation, except for the right shift test (test 2) for a small message. Copyright © 2001 John Wiley & Sons, Ltd.     

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。     

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。     

OpenFoam中多面体网格生成的MPI+OpenMP混合并行方法

网格生成是计算流体力学中非常重要的一环,大规模数值模拟过程中对网格精度要求的提高会导致网格生成所耗的时间增加。文中基于OpenFoam开源软件中的网格生成算法,主要研究多面体网格的并行生成,并提出OpenMP和MPI混合并行的多面体网格生成方法。通过理论分析得到,使用混合并行方法生成相同质量的网格时,混合并行方法生成网格的时间消耗随着线程数量和网格单元数量的增加而减少。3组使用不同求解器的数值模拟实验结果表明,该混合并行方法不但可以保证生成网格的质量——可以正常进行数值计算模拟且模拟结果与原方法相比几乎没有差别,而且生成同样质量与数量网格的耗时最多可以缩短至未使用OpenMP并行方法之耗时的1/4以内。     

Analysis of OpenMP and MPI implementations of meta-heuristics for vehicle routing problems

The parallelization of heuristic methods allows the researchers both to explore the solution space more extensively and to accelerate the search process. Nowadays, there is an increasing interest on developing parallel algorithms using standard software components that take advantage of modern microprocessors including several processing cores with local and shared cache memories. The aim of this paper is to show it is possible to parallelize algorithms included in computational software using standard software libraries in low-cost multi-core systems, instead of using expensive high-performance systems or supercomputers. In particular, it is analyzed the benefits provided by master-worker and island parallel models, implemented with MPI and OpenMP software libraries, to parallelize population-based meta-heuristics. The capacitated vehicle routing problem with hard time windows (VRPTW) has been used to evaluate the performance of these parallel strategies. The empirical results for a set of Solomon's benchmarks show that the parallel approaches executed on a multi-core processor produce better solutions than the sequential algorithm with respect to both the quality of the solutions obtained and the runtime required to get them. Both MPI and OpenMP parallel implementations are able to obtain better or at least equal solutions (in terms of distance traveled) than the best known ones for the considered benchmark instances.     

一种基于MPI与OpenMP的矩阵乘法并行算法

阐述MPI与OpenMP进行并行计算的特点,并在Visual Studio 2010上构建一个基于两者的混合编程平台。程序在该平台上执行时能够同时实现多进程与进程内多线程编程,设计并实现一种基于数据划分的矩阵乘法的并行算法,将数据分解为两部分交给两个计算节点分别完成,并在每个计算节点内将数据进一步划分,交给多个线程同时执行。通过与非并行矩阵乘法、MPI矩阵乘法、OpenMP矩阵乘法运算性能进行比较,验证该算法可以有效地挖掘计算机的处理能力。     

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．     

Design and Prototype of a Performance Tool Interface for OpenMP

This paper proposes a performance tools interface for OpenMP, similar in spirit to the MPI profiling interface in its intent to define a clear and portable API that makes OpenMP execution events visible to runtime performance tools. We present our design using a source-level instrumentation approach based on OpenMP directive rewriting. Rules to instrument each directive and their combination are applied to generate calls to the interface consistent with directive semantics and to pass context information (e.g., source code locations) in a portable and efficient way. Our proposed OpenMP performance API further allows user functions and arbitrary code regions to be marked and performance measurement to be controlled using new OpenMP directives. To prototype the proposed OpenMP performance interface, we have developed compatible performance libraries for the Expert automatic event trace analyzer [17, 18] and the TAU performance analysis framework [13]. The directive instrumentation transformations we define are implemented in a source-to-source translation tool called OPARI. Application examples are presented for both Expert and TAU to show the OpenMP performance interface and OPARI instrumentation tool in operation. When used together with the MPI profiling interface (as the examples also demonstrate), our proposed approach provides a portable and robust solution to performance analysis of OpenMP and mixed-mode (OpenMP+MPI) applications.     

High Performance RDMA-Based MPI Implementation over InfiniBand

Although InfiniBand Architecture is relatively new in the high performance computing area, it offers many features which help us to improve the performance of communication subsystems. One of these features is Remote Direct Memory Access (RDMA) operations. In this paper, we propose a new design of MPI over InfiniBand which brings the benefit of RDMA to not only large messages, but also small and control messages. We also achieve better scalability by exploiting application communication pattern and combining send/receive operations with RDMA operations. Our RDMA-based MPI implementation achieves a latency of 6.8 sec for small messages and a peak bandwidth of 871 million bytes/sec. Performance evaluation shows that for small messages, our RDMA-based design can reduce the latency by 24%, increase the bandwidth by over 104%, and reduce the host overhead by up to 22% compared with the original design. For large data transfers, we improve performance by reducing the time for transferring control messages. We have also shown that our new design is beneficial to MPI collective communication and NAS Parallel Benchmarks.     

MPI设计结构的分析与比较

MPICH与LAM是目前使用最广泛的MPI标准的实现。本文从设计思想和程序结构方面分析二者实现上的异同,重点比较可移植性和性能,并对二者最新版本进行了详细的性能测试。通过实例分析,提出设备层的设计要点,并为选择、移植和改进MPICH和LAM提出建议。     

基于MPI与OpenMP的飞机电磁软杀伤动态评估分析

针对国内外强电磁脉冲耦合效应分析现状及问题,阐述了电磁软杀伤概念,提出了基于共享存储OpenMP标准与消息传递MPI的时域有限差分-通用电路仿真程序FDTD-SPICE并行同步算法,建立了在电磁脉冲激励作用下的动态耦合模型,解决了飞机电磁软杀伤动态评估的难题;以某军机为例,开展了电磁武器的座舱耦合分布电磁场和FDTD-SPICE等效电路的并行同步仿真试验与防护评估分析.试验结果表明,该算法并行同步效果良好,军机在通电状态下,军机在通电状态下,幅度最值大大高于不通电条件下的幅度最值,易损性增强,防护效果减弱;防护座舱能大大减轻电磁武器对机载电路的影响.     

High performance computing using MPI and OpenMP on multi-core parallel systems

The rapidly increasing number of cores in modern microprocessors is pushing the current high performance computing (HPC) systems into the petascale and exascale era. The hybrid nature of these systems - distributed memory across nodes and shared memory with non-uniform memory access within each node - poses a challenge to application developers. In this paper, we study a hybrid approach to programming such systems - a combination of two traditional programming models, MPI and OpenMP. We present the performance of standard benchmarks from the multi-zone NAS Parallel Benchmarks and two full applications using this approach on several multi-core based systems including an SGI Altix 4700, an IBM p575+ and an SGI Altix ICE 8200EX. We also present new data locality extensions to OpenMP to better match the hierarchical memory structure of multi-core architectures.     

基于MPI＋OpenMP混合模型的并行地震数据处理支撑库的研究

计算机集群技术已经引起了石油地球物理界的广泛关注，如何将现有地震数据处理模块快速、高效地移植到集群上已成为地震数据处理需要解决的重大课题。本文将现有的基于消息传递（MPI）的并行地震处理模型与共享存储（OpenMP）模型相结合，实现了一个适合于SMP集群的并行地震数据支撑库，将涉及到消息传递的并行地震数据操作以直观的API的形式提供给开发人员。本文利用支撑库提供的API开发了一些测试模块。实验证明，支撑库可支持现有地震数据处理和显示模块的多种并行计算模型，并且能够获得较高的并行加速比和计算效率。     

基于MPI和OpenMP混合编程的非负矩阵分解并行算法

非负矩阵分解(NMF)作为一种数据降维和特征提取的有效工具,已经在文本聚类、推荐系统等多个领域得到应用,但是其计算过程比较复杂。对此,提出一种基于MPI+OpenMP的混合层次化并行NMF方法,其充分利用基于MPI的消息传递模型和基于OpenMP的共享存储模型各自的优势,并基于多核节点集群进行测试。实验结果表明,所设计的并行NMF算法达到了较高的加速比,能有效处理高阶矩阵的非负分解,极大地提高了计算的效率。     

PEMPIs: A New Methodology for Modeling and Prediction of MPI Programs Performance

The evaluation and prediction of parallel programs performance are becoming more and more important, so that they require appropriate techniques to identify the factors which influence the application execution time and also the way they interact. In this paper, we present some contributions of our research in this area by describing PEMPIs, a new methodology applied to the performance analysis and prediction of MPI programs. A new task graph helps us both to understand details of the application and to increase the accuracy of the prediction models. The proposed techniques are detailed and tested through the modeling of a complete application. PEMPIs efficiency has been proved by the results of this application modeling—most tests executed in a cluster of computers showed errors up to 10.