一种基于网格环境的远程过程调用系统的设计与分析

在分析已有的基于远程过程调用的网格计算框架的基础上,提出了一种采用移动代理技术的新的系统框架,与以前地系统框架相比,提高了网络环境的透明性。文中还尝试提供了对信息查询功能的支持。最后用实验方式证明了其可行性和实用性。     

RPCI：面向互联网的RPC框架

RPC是互联网后端分布式系统的核心组件,能够降低互联网应用开发、运维成本,提高可用性和可扩展性,但是目前流行的RPC框架不能完全满足互联网应用需求。分析了互联网应用环境下RPC系统的需求,并针对需求提出了面向互联网的RPC系统RPCI。RPCI采用三层架构,将长连接服务器独立出来,以支持无状态应用服务器设计和灵活的请求路由策略,使得系统扩容、升级、运维更加容易。基于thrift实现了RPCI,优化了性能,实验结果表明,RPCI性能优秀,相比常用开源软件thrift性能提升50％以上。     

用户态RPC协议分析及其多线程优化

RPC(Remote Procedure Call)协议有多个版本,分为内核态RPC与用户态RPC两类.开发人员根据设计需求选用合适的RPC版本,很多情况下会涉及跨内核态和用户态的通信.用户态RPC不如内核态RPC完善,缺少多线程机制、RDMA(Remote Direct Memory Access)机制等,需要优化以提高性能.结合此类需求,分析了用户态TI-RPC(Transport Independent Remote Procedure Call)运行机制,提出分层多线程的优化方法;利用TI-RPC底层接口重构RPC端口创建与服务启动;增加线程池机制,使TI-RPC在RPC层实现多线程并发服务.性能对比测试表明RPC内部多线程优化可将网络的利用率提高到网络满带宽的93%.     

Actors: A unifying model for parallel and distributed computing

Parallel computing and distributed computing have traditionally evolved as two separate research disciplines. Parallel computing has addressed problems of communication-intensive computation on tightly-coupled processors while distributed computing has been concerned with coordination, availability, timeliness, etc., of more loosely coupled computations. Current trends, such as parallel computing on networks of conventional processors and Internet computing, suggest the advantages of unifying these two disciplines. Actors provide a flexible model of computation which supports both parallel and distributed computing. One may evaluate the utility of a programming paradigm in terms of four criteria: expressiveness, portability, efficiency, and performance predictability. We discuss how the Actor model and programming methods based on it support these goals. In particular, we provide an overview of the state of the art in Actor languages and their implementation. Finally, we place this work in the context of recent developments in middleware, the Java language, and agents.     

SmartGridRPC: The new RPC model for high performance Grid computing

The paper presents the SmartGridRPC model, an extension of the GridRPC model, which aims to achieve higher performance. The traditional GridRPC provides a programming model and API for mapping individual tasks of an application in a distributed Grid environment, which is based on the client‐server model characterized by the star network topology. SmartGridRPC provides a programming model and API for mapping a group of tasks of an application in a distributed Grid environment, which is based on the fully connected network topology. The SmartGridRPC programming model and API and its performance advantages over the GridRPC model are outlined in this paper. In addition, experimental results using a real‐world application are also presented. Copyright © 2010 John Wiley & Sons, Ltd.     

PRESTO: A system for object-oriented parallel programming

PRESTO is a programming system for writing object-oriented parallel programs in a multiprocessor environment. PRESTO provides the programmer with a set of pre-defined object types that simplify the construction of parallel programs. Examples of PRESTO objects are threads, which provide fine-grained control over a program's execution, and synchronization objects, which allow simultaneously executing threads to co-ordinate their activities. The goals of PRESTO are to provide a programming environment that makes it easy to express concurrent algorithms, to do so efficiently, and to do so in a manner that invites extensions and modifications. The first two goals, which are the focus of this paper, allow a programmer to use parallelism in a way that is naturally suited to the problem at hand, rather than being constrained by the limitations of a particular underlying kernel or hardware architecture. The third goal is touched upon but not emphasized in this paper. PRESTO is written in C++; it currently runs on the Sequent shared-memory multiprocessor on top of the Dynix operating system. In this paper we describe the system model, its applicability to parallel programming, experiences with the initial implementation, and some early performance measurements.     

基于高性能计算的开源云平台性能评估

云计算是一种提供各种IT服务的互联网资源利用的新模式,已经广泛地应用在包括高性能计算的各种领域。然而,虚拟化带来了一些性能开销;同时,不同的云平台实施虚拟化技术的不同,使得在这些云平台上应用高性能计算服务的性能也千差万别。通过HPC Challenge (HPCC) Benchmark和NAS Parallel Benchmark(NPB)分别对CPU、内存、网络、扩展性和高性能计算真实负载进行评估,比较并分析了诸如Nimbus、OpenNebula和OpenStack实施高性能计算的性能,实验显示OpenStack对计算密集型的高性能应用负载表现出较好的性能,因此,OpenStack是实施高性能计算的开源云平台的一个好的选择。     

基于远程过程调用和壁垒同步的分布式离散事件仿真模型

针对多处理器高数据量情况的并行离散事件仿真提出了一种高效的仿真模型。仿真采用时间步进的推进方式,运用远程过程调用(RPC)分布调用机制实现仿真成员之间的互操作。仿真中采用壁垒同步机制保证仿真推进时的时间同步,以确保因果关系正确。实践证明,在保证了仿真准确性的前提下,该模型可对传输层和网络层协议进行仿真测试,能够对大数据量进行准确实时的处理,并在一定程度上提高了仿真效率。     

The design of an operating system for a scalable parallel computing engine

There are substantial benefits to be gained from building computing systems from a number of processors working in parallel. One of the frequently-stated advantages of parallel and distributed systems is that they may be scaled to the needs of the user. This paper discusses some of the problems associated with designing a general-purpose operating system for a scalable parallel computing engine and then describes the solutions adopted in our experimental parallel operating system. We explain why a parallel computing engine composed of a collection of processors communicating through point-to-point links provides a suitable vehicle in which to realize the advantages of scaling. We then introduce a parallel-processing abstraction which can be used as the basis of an operating system for such a computing engine. We consider how this abstraction can be implemented and retain the ability to scale. As a concrete example of the ideas presented here we describe our own experimental scalable parallel operating-system project, concentrating on the Wisdom nucleus and the Sage file system. Finally, after introducing related work, we describe some of the lessons learnt from our own project.     

A case study on expressiveness and performance of component-oriented parallel programming

Component-oriented programming has been applied to address the requirements of large-scale applications from computational sciences and engineering that present high performance computing (HPC) requirements. However, parallelism continues to be a challenging requirement in the design of CBHPC (Component-Based High Performance Computing) platforms. This paper presents strong evidence about the efficacy and the efficiency of HPE (Hash Programming Environment), a CBHPC platform that provides full support for parallel programming, on the development, deployment and execution of numerical simulation code onto cluster computing platforms.     

A survey on parallel and distributed multi-agent systems for high performance computing simulations

Simulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are often used to model and simulate complex systems. In any cases, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations and their execution on high performance resources as parallel clusters. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis of several multi-agent platforms, their tests in high performance computing execution environments, and the performance results for the only two platforms that fulfill the high performance computing constraints.     

Using high performance Fortran for parallel programming

A finite element code with a polycrystal plasticity model for simulating deformation processing of metals has been developed for parallel computers using High Performance Fortran (HPF). The conversion of the code from an original implementation on the Connection Machine systems using CM Fortran is described. The sections of the code requiring minimal inter-processor communication are easily parallelized, by changing only the syntax for specifying data layout. However, the solver routine based on the conjugate gradient method required additional modifications, which are discussed in detail. The performance of the code on a massively parallel distributed-memory Intel PARAGON supercomputer is evaluated through timing statistics. Published by Elsevier Science Ltd.     

A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling

This work presents a novel parallel micro evolutionary algorithm for scheduling tasks in distributed heterogeneous computing and grid environments. The scheduling problem in heterogeneous environments is NP-hard, so a significant effort has been made in order to develop an efficient method to provide good schedules in reduced execution times. The parallel micro evolutionary algorithm is implemented using MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on both well-known problem instances and large instances that model medium-sized grid environments. The comparative study of traditional methods and evolutionary algorithms shows that the parallel micro evolutionary algorithm achieves a high problem solving efficacy, outperforming previous results already reported in the related literature, and also showing a good scalability behavior when facing high dimension problem instances.     

Parallel application performance on shared high performance reconfigurable computing resources

The use of a network of shared, heterogeneous workstations each harboring a reconfigurable computing (RC) system offers high performance users an inexpensive platform for a wide range of computationally demanding problems. However, effectively using the full potential of these systems can be challenging without the knowledge of the system's performance characteristics. While some performance models exist for shared, heterogeneous workstations, none thus far account for the addition of RC systems. Our analytic performance model includes the effects of the reconfigurable device, application load imbalance, background user load, basic message passing communication, and processor heterogeneity. The methodology proves to be accurate in characterizing these effects for applications running on shared, homogeneous, and heterogeneous HPRC resources. The model error in all cases was found to be less than 5% for application runtimes greater than 30 s, and less than 15% for runtimes less than 30 s.     

基于网格计算平台的并行计算系统研究与实现

把网格和高性能计算结合起来,基于网格高性能计算平台的并行计算系统(GPCS),主要介绍了GPCS的体系结构、功能及其设计、实现等几个主要的问题。该平台以通用网络为基础,以网格平台中间件为桥梁,实现了各种高性能计算资源之间的互联互通、共享和协同工作。     

Design of a Remote Procedure Call system for object-oriented distributed programming

This paper describes the design of an RPC system for building object-oriented distributed software systems. The general requirements for such a system are to provide mechanisms for supporting inheritance, polymorphism, dynamic binding, and modular development in implementing distributed software systems. This paper presents the functionalities of this RPC system to support these general requirements. It also briefly describes the experiences with two previous versions of this system and how they led to the design of the final system. This system was implemented as a part of the programming environment of the Nexus distributed operating system. © 1998 John Wiley & Sons, Ltd.     

Care HPS: A high performance simulation tool for parallel and distributed agent-based modeling

Parallel and distributed simulation is a powerful tool for developing complex agent-based simulation. Complex simulations require parallel and distributed high performance computing solutions. It is necessary because their sequential solutions are not able to give answers in a feasible total execution time. Therefore, for the advance of computing science, it is important that High Performance Computing (HPC) techniques and solutions be proposed and studied. In literature, we can find some agent-based modeling and simulation tools that use HPC. However, none of these tools are designed to enable the HPC expert to be able to propose new techniques and solutions without great effort. In this paper, we introduce Care High Performance Simulation (HPS), which is a scientific instrument that enables researchers to: (1) develop techniques and solutions of high performance distributed simulations for agent-based models; and, (2) study, design and implement complex agent-based models that require HPC solutions. Care HPS was designed to easily and quickly develop new agent-based models. It was also designed to extend and implement new solutions for the main issues of parallel and distributed solutions such as: synchronization, communication, load and computing balancing, and partitioning algorithms. We conducted some experiments with the aim of showing the completeness and functionality of Care HPS. As a result, we show that Care HPS can be used as a scientific instrument for the advance of the agent-based parallel and distributed simulations field.     

Machines and models for parallel computing

It is widely believed that superscalar and superpipelined extensions of RISC style architecture will dominate future processor design, and that needs of parallel computing will have little effect on processor architecture. This belief ignores the issues of memory latency and synchronization, and fails to recognize the opportunity to support a general semantic model for parallel computing. Efforts to extend the shared-memory model using standard microprocessors have led to systems that implement no satisfactory model of computing, and present the programmer with a difficult interface on which to build parallel computing applications. A more satisfactory model for parallel computing may be obtained on the basis of functional programming concepts and the principles of modular software construction. We recommend that designs for computers be built on such a general semantic model of parallel computation. Multithreading concepts and dataflow principles can frame the architecture of these new machines.     

A comparative workload-based methodology for performance evaluation of parallel computers

A practical methodology for evaluating and comparing the performance of distributed memory Multiple Instruction Multiple Data (MIMD) systems is presented. The methodology determines machine parameters and program parameters separately, and predicts the performance of a given workload on the machines under consideration. Machine parameters are measured using benchmarks that consist of parallel algorithm structures. The methodology takes a workload-based approach in which a mix of application programs constitutes the workload. Performance of different systems are compared, under the given workload, using the ratio of their speeds. In order to validate the methodology, an example workload has been constructed and the time estimates have been compared with the actual runs, yielding good predicted values. Variations in the workload are analysed in terms of increase in problem sizes and changes in the frequency of particular algorithm groups. Utilization and scalability are used to compare the systems when the number of processors is increased. It has been shown that performance of parallel computers is sensitive to the changes in the workload and therefore any evaluation and comparison must consider a given user workload. Performance improvement that can be obtained by increasing the size of a distributed memory MIMD system depends on the characteristics of the workload as well as the parameters that characterize the communication speed of the parallel system.     

优化并行计算的性能评价

传统的并行计算的性能评价模型是加速比,文中讨论了加速比的缺点和不足,在此基础上提出了一种新的优化并行计算的性能评价模型（我们称之为优化加速比）。利用优化加速比分析了NAS基准测试程序MG和FT在IBM SP2(66mhz/wn)上的性能。