ORBIT: A parallel computing model of Prolog

This paper proposes a parallel processing model of the Prolog language. The model modifies Or-parallelism by introducing the “process bundle” as a candidate for simultaneous execution. The Process bundle is a subset of backtrack points stacked in depth-first execution. The process bundle includes one or more backtrack points, so it provides a longer process life cycle than the Or-parallel process. A process bundle is dispatched when an idle processor requests a job from an executing processor. The executing processor dispatches a message containing the full environment by which the idle processor can execute the process without any communication with other processors.     

The socio-ecological-economic model of a region in parallel computing

A general procedure of approximate optimal control synthesis for the socio-ecological-economic model of a region is developed. Program system DSEEmodel 1.0 is created, which involves a cluster computing device to implement parallel algorithms of scenario calculations, optimization and improvement of an approximate optimal control for the socio-ecological-economic model of a region. The program system serves for conducting multi-scenario calculations to design a sustainable development strategy for a region. In general, this is a new approach to the problem of situational control of a region, which employs supercomputers to implement the full-scale socio-ecological-economic model.     

Performance analysis and optimization on a parallel atmospheric general circulation model code

An analysis is presented of the primary factors influencing the performance of a parallel implementation of the UCLA atmospheric general circulation model (AGCM) on distributed-memory, massively parallel computer systems. Several modifications to the original parallel AGCM code aimed at improving its numerical efficiency, load-balance and single-node code performance are discussed. The impact of these optimization strategies on the performance on two of the state-of-the-art parallel computers, the Intel Paragon and Cray T3D, is presented and analyzed. It is found that implementation of a load-balanced FFT algorithm results in a reduction in overall execution time of approximately 45% compared to the original convolution-based algorithm. Preliminary results of the application of a load-balancing scheme for the physics part of the AGCM code suggest that additional reductions in execution time of 10–15% can be achieved. Finally, several strategies for improving the single-node performance of the code are presented, and the results obtained thus far suggest that reductions in execution time in the range of 35–45% are possible. © 1998 John Wiley & Sons, Ltd.     

Hybrid-WSI: a massively parallel computing technology?

It is argued that although it is not yet clear which of the two wafer scale integration (WSI) forms, monolithic or hybrid, will gain the lead to an enabling technology for second-generation massively parallel computers (MPCs), there are noticeably more backers for hybrid-WSI. The application requirements, implementation problems, and engineering issues of MPCs are discussed. In particular, the associative string processor (ASP) modules, which comprise building blocks for second-generation MPC configurations, are described. The progress reported in developing ASP modules is quantitatively extrapolated to other MPC implementations     

Extreme-scale parallel computing: bottlenecks and strategies

Extreme-scale numerical simulations seriously demand extreme parallel computing capabilities. To address the challenges of these capabilities toward exascale, we systematically analyze the major bottlenecks of parallel computing research from three perspectives: computational scale, computing efficiency, and programming productivity. For these bottlenecks, we propose a series of urgent key issues and coping strategies. This study will be useful in synchronizing development between the numerical computing capability and supercomputer peak performance.     

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.     

ViennaX: a parallel plugin execution framework for scientific computing

We present the free open source plugin execution framework ViennaX for modularizing and parallelizing scientific simulations. In general, functionality is abstracted by the notion of a task, which is implemented as a plugin. The plugin system facilitates the utilization of both, already available functionality as well as new implementations. Each task can define arbitrary data dependencies which are used by ViennaX to build a task graph. The framework supports the execution of this dependence graph based on the message passing interface in either a serial or a parallel fashion. The applied modular approach allows for defining highly flexible simulations, as plugins can be easily exchanged. The framework’s general design as well as implementation details are discussed. Applications based on the Mandelbrot set and the solution of a partial differential equation are investigated, and performance results are shown.     

WAPM:适合广域分布式计算的并行编程模型

早期的MPI与OpenMP等编程模型由于扩展性限制或并行粒度的差异而不适合于大规模的广域动态Internet环境.提出了一个用于广域网络范围内的并行编程模型(WAPM),为应用的分布式计算的编程提供了一个新的可行解决方案.WAPM由通信库、通信协议和应用编程接口组成,并且具有通用编程、自适应并行、容错性等特点,通过选择合适的编程语言,就可形成一个广域范围内的并行程序设计环境.以分布式计算平台P2HP为工作平台,描述了WAPM分布式计算的实施过程.实验结果表明,WAPM是一个通用的、可行的、性能较好的编程模型.     

PARCSIM: a parallel computing simulator for scalable software optimization

The Journal of Supercomputing - PARCSIM is a parallel software simulator that allows a user to capture, through a graphical interface, matrix algorithm schemes that solve scientific problems. With...     

Investigating Apache Hama: a bulk synchronous parallel computing framework

The quantity of digital data is growing exponentially, and the task to efficiently process such massive data is becoming increasingly challenging. Recently, academia and industry have recognized the limitations of the predominate Hadoop framework in several application domains, such as complex algorithmic computation, graph, and streaming data. Unfortunately, this widely known map-shuffle-reduce paradigm has become a bottleneck to address the challenges of big data trends. The demand for research and development of novel massive computing frameworks is increasing rapidly, and systematic illustration, analysis, and highlights of potential research areas are vital and very much in demand by the researchers in the field. Therefore, we explore one of the emerging and promising distributed computing frameworks, Apache Hama. This is a top level project under the Apache Software Foundation and a pure bulk synchronous parallel model for processing massive scientific computations, e.g. graph, matrix, and network algorithms. The objectives of this contribution are twofold. First, we outline the current state of the art, distinguish the challenges, and frame some research directions for researchers and application developers. Second, we present real-world use cases of Apache Hama to illustrate its potential specifically to the industrial community.     

Towards a portable and efficient environment for parallel computing

The availability of more and more cost-effective and powerful parallel computers has enhanced the ability of the operations research community to solve more laborious computational problems. In this paper an attempt has been made to implement a parallel simulation runs dispatcher with an objective to study the feasibility of establishing a portable and efficient parallel programming environment. This parallel simulation run dispatcher can be applied to both terminating type and steady-state type simulation models. The algorithm is then transferred and executed on various other shared-memory multiprocessor systems to illustrate its portability. Another contribution of this paper is to verify whether the performance of the portable code and the non-portable code of a same algorithm is significantly different on a specific parallel system using the analysis of covariance model.     

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.     

Anonymous remote computing: a paradigm for parallel programming oninterconnected workstations

Parallel computing on interconnected workstations is becoming a viable and attractive proposition due to the rapid growth in speeds of interconnection networks and processors. In the case of workstation clusters, there is always a considerable amount of unused computing capacity available in the network. However, heterogeneity in architectures and operating systems, load variations on machines, variations in machine availability, and failure susceptibility of networks and workstations complicate the situation for the programmer. In this context, new programming paradigms that reduce the burden involved in programming for distribution, load adaptability, heterogeneity and fault tolerance gain importance. This paper identifies the issues involved in parallel computing on a network of workstations. The anonymous remote computing (ARC) paradigm is proposed to address the issues specific to parallel programming on workstation systems. ARC differs from the conventional communicating process model by treating a program as one single entity consisting of several loosely coupled remote instruction blocks instead of treating it as a collection of processes. The ARC approach results in distribution transparency and heterogeneity transparency. At the same time, it provides fault tolerance and load adaptability to parallel programs on workstations. ARC is developed in a two-tiered architecture consisting of high level language constructs and low level ARC primitives. The paper describes an implementation of the ARC kernel supporting ARC primitives     

Posix: a model for future computing

The author considers how the Posix reference model remains accurate while other standards are becoming obsolete. The reference model identifies four main interfaces at which standards really matter: application programming interface; user interface; data interface; and communications interface     

A global optimization strategy based on the Kriging surrogate model and parallel computing

Structural and Multidisciplinary Optimization - The computational efficiency and accuracy of the global solution are the main performance indicators of an optimization algorithm to solve the...