影像数据分布并行计算处理平台体系架构研究

遥感影像数据并行处理系统大多依赖于国外商用产品,而国内自主化并行计算处理系统的任务流程化支撑能力以及并行计算性能难以适应规模化生产。为此,基于Hadoop的HDFS,MapReduce集群并行架构、CPU和GPU协同并行处理、内存映像、BMP等技术,提出流程驱动执行的高性能分布式并行计算处理平台体系架构。实验结果表明,工作站集群和工作站内多粒度混合的并行计算架构提高了平台并行处理性能,为海量遥感影像数据产品的批量生产提供一种自主化解决方案。     

工作站网络上协作任务的调度

在利用工作站群集系统进行的协作模式并行计算中，任务调度在很大程度上决定并行计算的性能．本文给出了一个任务调度的模型和算法，它考虑了协作模式并行计算中任务间的同步时间、通信时间、数据加载及结果收集时间．根据这个调度模型，可以选择一组并行执行时间最短的工作站，从而获得较好的并行计算性能．     

NOW环境下并行计算中的通信时延问题

1引言 工作站网络(NOW,Network Of Workstations)是一组专用或通用的计算机特别是性能工作站通过网络连成的计算机系统,由于NOW用于并行计算的主要资源是工作站,所以它被称为工作站群     

局域网分布式并行计算环境的建立及应用

主要介绍了以PVM系统作为局域网并行计算平台,在工作站机群上,建立了局域网分布式并行计算环境,简单介绍了在此并行环境中开发的多层二维二相油藏数值模拟的并行计算软件,利用工作站机群开发并行油藏模拟软件是为了探索解决大规模油藏模拟的行之有效的方法。     

网络环境下的并行计算模型

1.引言所谓网络并行计算就是将一群计算机系统用网络以某种结构互连起来,充分利用各系统资源,统一调度,协调处理,以实现高效的并行处理。它是并行分布式计算领域近年来比较活跃的研究领域。由于网络技术的发展,特别是局域网中高速数据通讯网络的出现,使得利用工作站集群(NOW或COW)作为并行计算的平台越来越具有吸引力,同时也出现了许多支持异种机网络计算的软件工具环境,如MPI、PVM、EXPRESS等。NOW与巨型机和MPP系统相比,具有很高的性能价格比,可扩展性     

一种基于Linux工作站网络的分布并行计算的设计方法

基于工作站网络的分布并行计算是并行计算的一个重要发展方向。而Linux系统是目前有发展前途的支持并发开放的操作系统。文章提出了一种在Linux工作站网环境下,基于CORBA技术和多进程编程实现分布并行计算的程序设计方法,经过实例测试,表明该方法具有实用价值。     

PVFS文件系统吞吐率分析和改进

1.引言近年来工作站机群系统以其低成本、高性能而日益成为高性能计算的潮流。尽管机群系统为许多应用提供丁高性能的计算平台,但是其相对低速的文件系统成为了系统的瓶颈,因此提高机群文件系统的性能具有重要的意义。吞吐率是指在单位时间内所处理的作业数,它是评价系统性能的一个重要指标。PVFS是一个机群文件系统,目前在大规模并行计算     

CUDA并行技术与数字图像几何变换

CUDA是GPU通过并发执行多个线程以实现大规模快速并行计算能力的技术,它能使对GPU编程变得更容易。介绍了CUDA基本特性及主要编程模型,在此基础上,提出并实现了基于NVIDIA CUDA技术的图像快速几何变换。采用位置偏移增量代替原变换算法中大量乘法运算,并把CUDA技术的快速并行计算能力应用到数字图像几何变换中,解决了基于CPU的传统图像几何变换运算效率低下的问题。实验结果证明使用CUDA技术,随着处理图像尺寸的增加,对数字图像几何变换处理效率最高能够提高到近100倍。     

工作站市场风起云涌

IBM增强RS/6000性能 IBM公司宣布将增强其RS/6000工作站产品系列的性能。新的系统以及现有系统的升级版将采用233MHz的PowerPC 604e微处理器,该公司还推出了新的4.5GB和9.1GB UltraSCSI接口的16位磁盘驱动器,以满足用户对性能的需求。新的Serial Storage Architecture选项将使用户能够对PCI SSA Four-Port RAID适配器进行扩充。 HP推出新的图形技术 HP公司技术计算业务部计划在今年底宣布一种能提高工作站图形处理能力的技术,它通过改变处理器处理与图形相关应用的方法来提高工作站的图形能力。这种新技术将采用并行处理器技     

国家重点实验室高速局域网论证与实施

一、安装高速网的必要性科学与工程计算国家重点实验室承担着科学与工程计算领域中具有重大意义的基础理论研究，解决重大计算及科学可视化问题。网络分布式并行计算处理中必须在相关的任务间交换数据，进行信息传递、高速网络技术使工作站之间的进程通讯加快，提高并行计算程序执行的速度，加入网络计算的计算机数目的增加对网络速度及可靠性提出了更高的要求。计算机网络性能的飞跃发展为采用网上计算机群并行计算来实现大规模计算问题及多媒体应用开辟了新的前景，保护了计算机与网络的投资。实验室于95年9月建成采用最先进的网络技术异…     

一种针对结构化并行控制机制的任务调度算法

缩短程序的执行时间是并行处理的首要目标,有效的任务分配算法是实现这一目标的关键,对机群系统来说更是如此.研究机群系统上针对结构化并行控制机制的任务调度问题,并基于贪心算法、粒度控制、反馈式分派的原则,提出近优的任务调度算法SSA(sub-optimal scheduling algorithm).实验结果表明,在机群环境下,该算法的并行计算性能与其他算法相比均有所提高.     

分布异构工作站上的任务调度算法

讨论了在一个由高速局域网连接的高性能异构工作站平台上，如何有效地利用空闲工作站来求解计算密集型任务矩阵相乘的问题，为了获得较好的并行计算性能，文中给出了一个异构工作站群之间任务调度的模型和算法，算法中考虑了并行计算中协作任务间的通信时间、数据加栽时间、结果收集时间和各个异构工作站的任务计算时间，通过这个模型，可以在所有可利用的工作站集合中找出最适合的子集，获得最短的执行时间．     

Limitations of Cycle Stealing for Parallel Processing on a Network of Homogeneous Workstations

The low cost and availability of clusters of workstations have lead researchers to re-explore distributed computing using independent workstations. This approach may provide better cost/performance than tightly coupled multiprocessors. In practice, this approach often utilizes wasted cycles to run parallel jobs. In this paper we address the feasibility and limitation of such a nondedicated parallel processing environment assuming workstation processes have priority over parallel tasks. We develop a simple analytical model to predict parallel job response times. Our model provides insight into how significantly workstation owner interference degrades parallel program performance. It forms a foundation for task partitioning and scheduling in a nondedicated network environment. A new term, task ratio, which relates the parallel task demand to the mean service demand of nonparallel workstation processes, is introduced. We propose that task ratio is a useful metric for determining how a parallel applications should be partitioned and scheduled in order to make efficient use of a nondedicated distributed system.     

A portable fault-tolerant parallel software MPEG-1 encoder

MPEG video compression is quite difficult to achieve in real time, and hardware solutions for this problem are expensive. We present a portable, fault-tolerant, parallel, MPEG-1 encoder implemented in software. We detail the implementation strategy for the encoder and give performance results on a network of workstations and a massively parallel processor. We also show that our encoder can efficiently adapt to fluctuating processing power typical in workstation networks.     

High performance computing on networks of workstations through the exploitation of function parallelism

Network of workstations (NOW) has become a widely accepted form of high-performance parallel computing. As in conventional multicomputers, parallel programs running on such a platform are often written in an SPMD form to exploit data parallelism. Each workstation in a NOW is treated similarly to a processing element in a multicomputer system. However, workstations are far more powerful and flexible than the processing elements in conventional multicomputers. In this paper, we discuss how workstations in a NOW can be used to exploit more parallelism in an SPMD program, especially those induced from concurrent activities.     

Coupling hundreds of workstations for parallel molecular sequence analysis

We present a highly scalable approach to distributed parallel computing on workstations in the Internet which provides significant speed-up to molecular biology sequence analysis. Recent developments show that smaller numbers of workstations connected via a local area network can be used efficiently for parallel computing. This work emphasizes scalability with respect to the number of workstations employed. We show that a massively parallel approach using several hundred workstations, dispersed over all continents, can successfully be applied for solving problems with low requirements on communication bandwidth. We calculated the optimal local alignment scores between a single genetic sequence and all sequences of a genetic sequence database using the ssearch code that is well known among molecular biologists. In a heterogeneous network with more than 800 workstations this job terminated after several minutes, in contrast to several days it would have taken on a single machine.     

Coordinating Parallel Processes on Networks of Workstations

The network of workstations (NOW) we consider for scheduling is heterogeneous and nondedicated, where computing power varies among the workstations and local and parallel jobs may interact with each other in execution. An effective NOW scheduling scheme needs sufficient information about system heterogeneity and job interactions. We use the measured power weight of each workstation to quantify the differences of computing capability in the system. Without a processing power usage agreement between parallel jobs and local user jobs in a workstation, job interactions are unpredictable, and performance of either type of jobs may not be guaranteed. Using the quantified and deterministic system information, we design a scheduling scheme calledself-coordinated local schedulingon a heterogeneous NOW. Based on a power usage agreement between local and parallel jobs, this scheme coordinates parallel processes independently in each workstation based on the coscheduling principle. We discuss its implementation on Unix System V Release 4 (SVR4). Our simulation results on a heterogeneous NOW show the effectiveness of the self-coordinated local scheduling scheme.     

Dynamic mapping of cooperating tasks to nodes in a distributed system

Networks of workstations (NOWs) are a low-cost and widespread platform for parallel computing. This paper focuses on the dynamic task-scheduling problem in NOW environments. The aim is to minimize the completion time of parallel programs by distributing cooperating concurrent tasks to homogeneous networked nodes. Cooperation dependencies as well as creation and termination dependencies between tasks are taken into account. An event lattice model is introduced to describe past, actual and future behavior of a parallel program in execution. Based on this model an algorithm is presented to dynamically assign tasks to the nodes of a dedicated distributed system. Crucial for the efficiency of this approach is a top-down construction of all operating system entities involved in distributed resource management, particularly the close cooperation of the compiler and runtime system, which allows the creation of event lattice clippings at runtime.     

Advancing parallel solution phase library synthesis through efficient purification, quantitation, and characterization techniques

Historically, access to large numbers of quality compounds in a parallel solution phase library synthesis environment has been hindered by the inability to rapidly purify, quantitate, and characterize solution phase libraries in an efficient and cost effective manner. At Neurogen Corporation, we overcome these barriers by employing a collection of novel workstations in the High Speed Synthesis (HSS) group that are tightly integrated into our informatics architecture. This approach supports rapid and reliable instrument operation, rapid interpretation of results, and decision analysis for further downstream tasks and processing. Our movement of library samples and data from workstation to workstation facilitates synthesis throughput and the utilization of equipment, creating a cost-effective library production environment.     

A pipeline-based approach for scheduling video processing algorithms on NOW

Network Of Workstations (NOW) platforms put together with off-the-shelf workstations and networking hardware have become a cost effective, scalable, and flexible platform for video processing applications. Still, one has to manually schedule an algorithm to the available processors of the NOW to make efficient use of the resources. However, this approach is time-consuming and impractical for a video processing system that must perform a variety of different algorithms, with new algorithms being constantly developed. Improved support for program development is absolutely necessary before the full benefits of parallel architectures can be realized for video processing applications. Toward this goal, an automatic compile-time scheduler has been developed to schedule input tasks of video processing applications with precedence constraints onto available processors. The scheduler exploits both spatial (parallelism) and temporal (pipelining) concurrency to make the best use of machine resources. Two important scheduling problems are addressed. First, given a task graph and a desired throughput, a schedule is constructed to achieve the desired throughput with the minimum number of processors. Second, given a task graph and a finite set of available resources, a schedule is constructed such that the throughput is maximized while meeting the resource constraints. Results from simulations show that the scheduler and proposed optimization techniques effectively tackle these problems by maximizing processor utilization. A code generator has been developed to generate parallel programs automatically. The tools developed in this paper make it much easier for a programmer to develop video processing applications on these parallel architectures.