Parallel and Distributed Computing Environment

Legacy applications based on the Distributed Computing Environment (DCE) are subject to several significant limitations. As the development and support of DCE wanes, object‐oriented development becomes more desirable, and transmission over HTTP is established as the preferred protocol over the Internet, DCE application managers and developers are pressed to find extensions and alternatives to DCE. This paper briefly discusses several alternative targets for migration of DCE systems, then proceeds to detail, compare, and contrast two preferred candidates: CORBA and SOAP. Although we have found that developing a general migration solution for legacy DCE applications to CORBA or SOAP to be a non‐trivial, long‐term project, developing specific solutions that are based on a general architecture is feasible. Given a short list of reasonable premises, many DCE applications may be ported to technologies such as CORBA and SOAP. Copyright © 2002 John Wiley & Sons, Ltd.

This article presents a review of various hardware and software parallel computing environments so far developed for the implementation of parallel algorithms to solve complex and computationally dominated optimization problems. These parallel environments are composed of parallel programming tools, performance evaluation tools, debuggers and optimization libraries. The environments can be classified based on the ease of implementation, hardware independence, ease to understand, and guarantee of performance.

Dynamic responses considering fluid structure interaction (FSI) is important in many engineering fields and some of the FSI phenomena are treated as an acoustic fluid and structure interaction (AFSI) problem. The dynamic interactions between the fluid and structure can change dynamic characteristics of structures and their responses to external excitation such as seismic loading. The authors have developed a coupled simulation system for the large scale AFSI problems using an iterative partitioned coupling technique. In the system, the authors employed ADVENTURE system which adopted an efficient preconditioned iterative linear algebraic solver, and ADVENTURE Coupler is used to handle interface variable efficiently on various parallel computational environments. The authors employ Broyden method for updating interface accelerations to obtain the robust and fast convergence property of fixed point iterations. This paper presents the overview of the coupled analysis system and the results of its application to several AFSI problems are shown. The system runs efficiently in a parallel environment and it is capable for analyses of complex shaped three dimensional structures with more than 20 million degrees of freedom model.

This paper presents a toolkit for managing distributed communication in multi-application systems that are targeted to run in high performance computing environments; the Distributed Data Broker (DDB). The DDB provides a flexible mechanism for coupling codes with different grid resolutions and data representations. The target applications are coupled systems that deal with large volumes of data exchanges and/or are computational expensive. These application codes need to run efficiently in massively parallel computer environments generating a need for a distributed coupling to minimize long synchronization points. Furthermore, with the DDB, coupling is realized in a plug-in manner rather than hard-wire inclusion of any programming language statements. The DDB performance in the CRAY T3E-600 and T3E-900 systems is examinedKeywordsMMP systemsDistributed ComputingData Brokeragecoupling

Application of the parallel diagonal dominant algorithm for the incompressible Navier-Stokes equations

The demand for processing power has been ever increasing with the growth of high-performance computing (HPC) applications and so have the constraints restricting the solutions to such requirements. High-performance distributed and parallel computing and custom-built, hardware-based computing have attempted to address this problem with some success but at a substantial cost. Recently, systems augmented with Field-Programmable Gate Arrays (FPGAs) offering a fusion of traditional parallel and distributed machines with customizable and dynamically reconfigurable hardware have emerged as a cost-effective alternative to traditional systems. However, providing a robust runtime environment for such systems to which HPC users have become accustomed has been fraught with numerous challenges. Dynamic scheduling of large-scale HPC applications in such parallel reconfigurable computing (RC) environments is one such challenge and has not been sufficiently studied to our knowledge. In this paper, we simulatively analyze the performance of several common HPC scheduling heuristics that can be used by an automated job management service to schedule application tasks on a parallel RC system. We also present a performance prediction model which the scheduling heuristics employ to schedule several common HPC applications on a collection of typical FPGA processing platforms.

This paper shows that graph traversal techniques have fundamental differences between serial and distributed computations in their behaviors, computational complexities, and effects on the design of graph algorithms. It has three major parts. Section I describes the computational environment for the design and description of distributed graph algorithms in terms of an architectural model for message exchanges. The computational complexity is measured in terms of the number of messages transmitted. Section II presents several distributed algorithms for the pure traversal, depth-first search, and breadth-first search techniques. Their complexities are also given. Through these descriptions are brought out some of the intrinsic differences in the behaviors and complexities of the fundamental traversal techniques between a serial and a distributed computation environment. Section III gives the distributed version of the Ford and Fulkerson algorithm for the maximum flow problem by means of depth-first search, the largest-augmentation search and breadth-first search. The complexities of these methods are found to be 0(f*|A|), 0((l + logM/(M-1)f*|V||A|) and O(|V|6), respectively, where f* is the maximum flow value of the problem, M is the maximum number of ucs in a cut, |V| is the number of vertices, and |A| is the number of arcs. Lastly, it is shown that the largest augmentation search may be a better method than the other two. This is contrary to the known results in serial computation.

In theDistributed Computing Environment, many processors/nodes remain active in system at any moment, coordinate or operate without interfering. Nodes have certain degree of freedom like own hardware, own code, sometimes ownindependent task, but at the same time there will still be sharing of some common resources and information. One of the main themes of recent research in distributed computing is "locality" (as utilized by decentralized/peer-to-peer systems). Networks grow fast, thus locality and scalability becomes a major issues. Computing security is, at its core, more than a technical issue. It is a fundamental business challenge. Managers have plenty of security alternatives, but have very little real guidance on making intelligent decisions about them. Today's distributed, multi-vendor, Internet-connected environments encompass more insecure systems and networks than ever before. Security in Distributed Computing offers the manager of distributed systems a thorough, common-sense framework for cost-effective computer security. The first step to understand security is to know what the potential risks are, or more specifically, to determine the type and level of security risks for the organizations. Security risks are unique to each organization because they are dependent on the nature of the business and the environment in which the organization operates. Security risk is determined by identifying the assets that need to be protected.

The paper addresses a new area of research-distributed system software maintenance-focusing on whether and to what extent distributed computing environments directly affect software maintenance difficulty. The issue appears to depend on two diametrics of information system architectures: component simplicity and system complexity. Using a new conceptual model for information systems complexity applied to distributed computing environments, this research sought empirical quantitative and qualitative data from IS professionals to determine whether the complexity of a distributed computer system has a greater effect on software maintenance than component simplicity, and what the explanatory factors are. Results indicate the overall complexity of studied distributed systems overwhelmed the ease-of-use and simplicity of their components-increasing the overall difficulty of software maintenance. Some factors had a greater effect than others; the rate of technological change and the variety of components (particularly processors and system software) far outweighed the effects of component and transaction numbers.

NeuroGraph is a simulation environment for design, construction and execution of neural networks and genetic algorithms in a distributed computing environment. The simulator parts either run on single computers or as distributed applications on UNIX/X-based networks consisting of personal computers, workstations or multi-processors. The parallelization component offers the possibility to divide computational tasks into concurrently executable modules, according to restrictions due to the neural net topology and computer net capabilities, i.e. NeuroGraph tries to select the best configuration out of the available distributed hardware environment to fit performance requirements.

Modern pace of Information and Communication Technologies (ICT) progress requires rapid development of completely new approaches to data processing. One of the most important criteria that should be taken into account in every computing system nowadays is its energy efficiency. Strict requirements to service availability and timeliness must be taken into account as well. In this paper new comprehensive energy efficient approach to workload processing in distributed computing environment is proposed. The goal of this approach is to get as close as possible to the energy-proportional computing model. Proposed approach combines the advantages of horizontal scaling and energy efficient scheduling taking into account individual power consumption characteristics of computing nodes and dynamicity of workload in modern computing systems. The efficiency of the proposed approach is proven using Matlab modeling.

When Evolutionary Algorithms (EAs) are used for Artificial Neural Networks (ANNs) training, the most valuable advantage is the potential for this training to be done in parallel or even using distributed computing. With the capabilities of modern mobile devices, for example their use for distributed computations, they can be used much more extensively for scientific calculations. It is well known that distributed computing systems are limited by their communication bandwidth, because of network latency. In such environment some EAs are pretty suitable for distributed implementation. This is because of their high level of parallelism and relatively less intensive network communication needs. Subset of distributed computing is volunteer computing where users donate some of the computing power provided by devices under their control. This research proposes Android Live Wallpaper volunteer computing implementation of a system used for financial time series prediction. The forecasting module is organized as ANN, which is trained by hybrid combination of Backpropagation and EAs.

Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments

Fully sequential ranking-and-selection (R&S) procedures to find the best from a finite set of simulated alternatives are often designed to be implemented on a single processor. However, parallel computing environments, such as multi-core personal computers and many-core servers, are becoming ubiquitous and easily accessible for ordinary users. In this paper, we propose two types of fully sequential procedures that can be used in parallel computing environments. We call them vector-filling procedures and asymptotic parallel selection procedures, respectively. Extensive numerical experiments show that the proposed procedures can take advantage of multiple parallel processors and solve large-scale R&S problems.

We describe total congestion 1 embeddings of complete binary trees into three dimensional grids with expansion ratios 1.172 and 1.25. That is, we give a one-to-one embedding of any complete binary tree into a hexahedron shaped grid such that: the number of nodes in the grid is at most 1.172 (1.25) times the number of nodes in the tree; and no tree nodes or edges occupy the same grid positions. The first strategy embeds trees into cube shaped 3D grids. That is, 3D grids in which all dimensions are roughly equal in size. The second strategy embeds trees into flat 3D grid shapes. That is, it maps complete binary trees into 3D grids with a fixed, small number of layers. An application suggests which embedding to use. For simulations in parallel computer environments, or possibly graph drawing, a cube shaped 3D grid is appropriate. For the sake of VLSI, or other graph drawing applications, embeddings with a small number of layers are better.