一个基于高性能机群系统的并行数据挖掘平台模型

基于高性能机群系统建立并行数据挖掘平台,能够有效地提高数据挖掘算法的执行效率,提高对大量数据信息的处理能力,而且可以减少系统开发、升级及维护的费用,提高系统可伸缩性。文章从机群系统的特性出发,论述了基于高性能机群系统建立并行数据挖掘平台的目标、方法和实现技术,给出了一个合理可行的平台模型。     

基于机群系统的C＋＋语言并行化实现

在计算机机群系统环境下，将面向对象程序设计技术与并行技术相结合能够有效地降低并行程序设计的难度，提高并行程序的可维护性、可移植性和可重用性．本文探讨了机群系统下的Ｃ＋＋语言并行化实现的几种方法，分别介绍了基于消息传递的ＭＰＣ＋＋、基于共享对象的ＳＯＣ＋＋和基于对象级并行的ＣＣＰＰ语言模型、编程接口及其实现，并给出了几种语言系统评测的结果及分析．     

一种简化的基于First-Fit的Backfilling调度策略——RB-FIFT

机群作业管理系统是机群系统的重要组成部分,而作业调度策略又是机群作业管理系统的核心内容。作业调度策略的选择不仅关系到机群系统的利用率,还决定用户作业的响应速度和平均执行时间。在节点分配基于空间共享策略(Space-Sharing)的机群系统中,传统的基于先来先服务的First-Fit调度策略虽然能够提高机群系统的利用率,却容易引起饥饿问题。文章基于传统的先来先服务的调度策略,提出了一种简化的Backfilling算法,简要叙述了该算法的设计和实现,最后根据模拟实验结果,从用户和系统的角度分析了该算法的性能。     

一种改进的机群SPMD编程模型静态任务分配算法

机群系统中，结点机差异及动态运行环境是导致系统运行状态不均衡的主要原因．提出了结点机差异及负载测量指标和方法．以SPMD编程模型中的单一并行任务为测试程序，在机群系统性能测试、分析的基础上，进行运行时间相同原则基础上的静态任务分配；在应用运行过程中，通过对并发线程运行状态测试，用已完成任务的高速设备重复执行低速机器未完成任务，选取最快运行结果．从而缩小不均衡因素的影响．以空间划分大规模Monte Carlo仿真问题为例进行算法实际测试，取得良好运行效果．     

机群环境下并行蒙特卡罗方法的研究与应用

在分布式存储结构的机群系统上，采用可移植消息传递接口MPI与C语言绑定，设计并实现了并行蒙特卡罗算法．有效解决了计算量大、串行算法执行时间长的问题。通过对机群节点间通信时间开销的研究分析．采用主从式编程模型改进并行蒙特卡罗算法，实现了负载平衡，提高了机群处理器的利用率，进一步缩短了执行时间。     

LUNF--基于节点失效特征的机群作业调度策略

良好的可扩展性使得人们可通过扩大机群系统的规模来达到所需要的计算能力,但随着机群系统节点数目的增多,节点失效对机群系统性能的影响已经成为大规模机群系统使用过程中一个不可回避的问题．机群作业调度作为机群操作系统软件的重要组成部分,完成高效资源管理与合理作业调度,机群作业调度系统功能上可分为作业选取策略和节点分配策略两部分．结合机群系统节点失效的特征,提出了正常运行时间最长节点优先(longest uptime node first,LUNF)的节点分配策略．仿真结果表明,相对于节点随机分配策略,LUNF策略的作业平均响应时间与作业平均slowdown降低10％左右．     

机群OpenMP系统的设计与实现

OpenMP以其易用性和支持增量并行的特点成为共享存储体系结构的编程标准．目前机群系统已成为高性能计算的主流平台,研究机群OpenMP系统对推进并行应用的开发和普及非常有意义．该文作者以软件DSM系统JIAJIA作为OpenMP的运行时系统,结合一个前端编译器OMP2JIA,在机群系统上实现了OpenMP／JIAJIA计算环境,同时在提高性能方面根据机群系统特点扩展了OpenMP制导,优化了后端运行时库。通过11个OpenMP应用,作者比较了该计算环境和一个支持OpenMP的硬件cc-NUMA系统(SGI 2100)的性能．结果表明,作者的机群OpenMP系统的7机平均加速比为4．62;SGI 2100系统为4．55,二者性能相当．     

Myrinet:一种适用于机群系统的新型高速网络

机群系统用于并行处理具有很多ＭＰＰ系统所没有的优点，但要实现高性能的机群系统，则需要高带宽、低延迟的高速网络的支持。该文从软硬件结构、采用的技术及其应用实例几方面介绍了一种适用于机群系统的新型高速网络———Ｍｙｒｉｎｅｔ，并通过实验测试数据以及与其它高速网络特性的对比可知，Ｍｙｒｉｎｅｔ的性能／价格比很高，是组建机群系统的理想网络解决方案。     

宽带网络入侵检测系统的分析与实现

提出并实现了一种采用新型体系结构的宽带网络入侵检测系统，该系统主要采用了高性能的宽带数据采集器技术和动态可扩展的机理机群系统技术。重点阐述了该系统的体系结构，宽带数据采集器的负载均衡算法，以及处理机群系统中的动态可扩展技术。     

机群管理系统消息传输的负载平衡

提出了一种机群管理系统中管理消息的传递方式，使管理消息以树形方式在机群系统内部扩散传递，根据当前系统状态动态生成消息的剩余传播路径，维持机群系统内部的负载平衡。     

Parallel Computing on an Ethernet Cluster of Workstations: Opportunities and Constraints

Parallel computing on clusters of workstations is receiving much attention from the research community. Unfortunately, many aspects of parallel computing over this parallel computing engine is not very well understood. Some of these issues include the workstation architectures, the network protocols, the communication-to-computation ratio, the load balancing strategies, and the data partitioning schemes. The aim of this paper is to assess the strengths and limitations of a cluster of workstations by capturing the effects of the above issues. This has been achieved by evaluating the performance of this computing environment in the execution of a parallel ray tracing application through analytical modeling and extensive experimentation. We were successful in illustrating the effect of major factors on the performance and scalability of a cluster of workstations connected by an Ethernet network. Moreover, our analytical model was accurate enough to agree closely with the experimental results. Thus, we feel that such an investigation would be helpful in understanding the strengths and weaknesses of an Ethernet cluster of workstation in the execution of parallel applications.     

Parallel multi-dimensional range query processing with R-trees on GPU

The general purpose computing on graphics processing unit (GP-GPU) has emerged as a new cost effective parallel computing paradigm in high performance computing research that enables large amount of data to be processed in parallel. Large scale scientific data intensive applications have been playing an important role in modern high performance computing research. A common access pattern into such scientific data analysis applications is multi-dimensional range query, but not much research has been conducted on multi-dimensional range query on the GPU. Inherently multi-dimensional indexing trees such as R-Trees are not well suited for GPU environment because of its irregular tree traversal. Traversing irregular tree search path makes it hard to maximize the utilization of massively parallel architectures. In this paper, we propose a novel MPTS (Massively Parallel Three-phase Scanning) R-tree traversal algorithm for multi-dimensional range query, that converts recursive access to tree nodes into sequential access. Our extensive experimental study shows that MPTS R-tree traversal algorithm on NVIDIA Tesla M2090 GPU consistently outperforms traditional recursive R-trees search algorithm on Intel Xeon E5506 processors.     

JASMIN框架中多块结构网格拼接并行计算及其应用

基于JASMIN框架,本文设计了多块结构网格拼接的并行算法并研制了相应的软件模块。该模块设计实现了网格块间关系统一描述算法,及网格片间统一通信调度策略,从而有效解决了多块结构网格拼接并行计算中的通信性能瓶颈问题。同时,该模块封装了数据分布存储、数据通信等并行计算细节,提供了规范接口,能够支撑用户简便地实现多块结构网格拼接并行计算。数值测试表明,该模块具有很好的并行性能,可以支撑应用程序扩展到上千核。     

基于工作站机群的有限元结构分析并行计算

并行计算正成为科学和工程计算中的一个新趋势。将采用区域分裂技术的并行有限元方法应用于工作站机群的分布式并行环境。提出了基于单元区域分裂的共轭梯度并行算法。在工作站机群上对坝体结构进行求解,对其并行性能进行分析。     

基于时间Petri网的多处理机的调度算法

任务调度是并行分布式计算机中最有挑战性的问题之一。如何合理有效地进行任务调度将直接影响到系统的并行效率。文中通过将任务图转换为时间petri网的方法,利用求时间petri网的可覆盖树的方法来分析网系统的状态变化和变迁的发生序列,从而求出关键路径和顺序队列。再将该队列分配到处理机上,来缩短相关任务图的调度长度。     

科学计算应用与流处理器

研究科学计算应用在流处理器上的适用性已成为当前研究热点之一。本文首先分析了流处理器处理科学计算应用的优势以及在流处理器上开发科学计算面临的重大挑战;然后针对不同类型的科学计算应用给出了将科学计算应用映射到流处理器上的关键与优化方法;最后将八种具有不同性能特征的典型科学计算应用映射到流处理器上,并比较和分析这些流程序在时钟精确模拟器运行性能和在处理科学计算应用的相应Fortran程序在主流Itanium 2处理器上的运行性能。实验结果表明,流处理器能有效处理科学计算应用。     

异构多核处理器体系结构设计研究

多核技术成为当今处理器发展的重要方向,异构多核处理器由于可将不同类型的计算任务分配到不同类型的处理器核上并行处理,从而为不同需求的应用提供更加灵活、高效的处理机制而成为当今研究的热点.本文从体系结构的角度探讨了异构多核处理器设计中的关键点,从内核结构、互连方式、存储系统、操作系统支持、测试与验证、动态电压调节等方面分析...     

Dynamic software updates for parallel high‐performance applications

Despite using multiple concurrent processors, a typical high‐performance parallel application is long‐running, taking hours, even days to arrive at a solution. To modify a running high‐performance parallel application, the programmer has to stop the computation, change the code, redeploy, and enqueue the updated version to be scheduled to run, thus wasting not only the programmer's time, but also expensive computing resources. To address these inefficiencies, this article describes how dynamic software updates (DSU) can be used to modify a parallel application on the fly, thus saving the programmer's time and using expensive computing resources more productively. The net effect of updating parallel applications dynamically can reduce the total time that elapses between posing a problem and arriving at a solution, otherwise known as time‐to‐discovery. To explore the benefits of dynamic updates for high performance applications, this article takes a two‐pronged approach. First, we describe our experiences of building and evaluating a system for dynamically updating applications running on a parallel cluster. We then review a large body of literature describing the existing state of the art in DSU and point out how this research can be applied to high‐performance applications. Our experimental results indicate that DSU have the potential to become a powerful tool in reducing time‐to‐discovery for high‐performance parallel applications. Copyright © 2010 John Wiley & Sons, Ltd.     

JASMIN: a parallel software infrastructure for scientific computing

The exponential growth of computer power in the last 10 years is now creating a great challenge for parallel programming toward achieving realistic performance in the field of scientific computing. To improve on the traditional program for numerical simulations of laser fusion in inertial confinement fusion (ICF), the Institute of Applied Physics and Computational Mathematics (IAPCM) initializes a software infrastructure named J Adaptive Structured Meshes applications INfrastructure (JASMIN) in 2004. The main objective of JASMIN is to accelerate the development of parallel programs for large scale simulations of complex applications on parallel computers. Now, JASMIN has released version 1.8 and has achieved its original objectives. Tens of parallel programs have been reconstructed or developed on thousands of processors. JASMIN promotes a new paradigm of parallel programming for scientific computing. In this paper, JASMIN is briefly introduced.     

JASMIN: a parallel software infrastructure for scientific computing

The exponential growth of computer power in the last 10 years is now creating a great challenge for parallel programming toward achieving realistic performance in the field of scientific computing. To improve on the traditional program for numerical simulations of laser fusion in inertial confinement fusion (ICF), the Institute of Applied Physics and Computational Mathematics (IAPCM) initializes a software infrastructure named J Adaptive Structured Meshes applications INfrastructure (JASMIN) in 2004. The main objective of JASMIN is to accelerate the development of parallel programs for large scale simulations of complex applications on parallel computers. Now, JASMIN has released version 1.8 and has achieved its original objectives. Tens of parallel programs have been reconstructed or developed on thousands of processors. JASMIN promotes a new paradigm of parallel programming for scientific computing. In this paper, JASMIN is briefly introduced.