Abstract. The Globus Toolkit (GT) has been developed since the late 1990s to support the development of service-oriented distributed computing applications and infrastructures. Core GT components address, within a common framework, basic issues relating to security, resource access, resource management, data movement, resource discovery, and so forth. These components enable a broader “Globus ecosystem ” of tools and components that build on, or interoperate with, core GT functionality to provide a wide range of useful application-level functions. These tools have in turn been used to develop a wide range of both “Grid ” infrastructures and distributed applications. I summarize here the principal characteristics of the latest release, the Web services-based GT4, which provides significant improvements over previous releases in terms of robustness, performance, usability, documentation, standards compliance, and functionality. 1

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Desktop grids have recently been used to perform some of the largest computations in the world and have the potential to grow by several more orders of magnitude. However, current approaches to utilizing desktop resources require either centralized servers or extensive knowledge of the underlying system, limiting their scalability.

The Grid is rapidly emerging as the dominant paradigm for wide area distributed application systems. As a result, there is a need for modeling and analyzing the characteristics and requirements of Grid systems and programming models. This paper adopts the well-established body of models for distributed computing systems, which are based upon carefully stated assumptions or axioms, as a basis for defining and characterizing Grids and their programming models and systems. The requirements of programming Grid applications and the resulting requirements on the underlying virtual organizations and virtual machines are investigated. The assumptions underlying some of the programming models and systems currently used for Grid applications are identified and their validity in Grid environments is discussed. A more in-depth analysis of two programming systems, the Imperial College E-Science Networked Infrastructure (ICENI) and Accord, using the proposed definitions’ structure is presented. Keywords—Distributed systems, Grid programming models, Grid programming systems, Grid system definition. I.

Cloud Computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established Grid Computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both.

Parallel computing is now popular and mainstream, but performance and ease-of-use remain elusive to many endusers. There exists a need for performance improvements that can be easily retrofitted to existing parallel applications. In this paper we present MPI process swapping, a simple performance enhancing add-on to the MPI programming paradigm. MPI process swapping improves performance by dynamically choosing the best available resources throughout application execution, using MPI process over-allocation and real-time performance measurement. Swapping provides fully automated performance monitoring and process management, and a rich set of primitives to control execution behavior manually or through an external tool. Swapping, as defined in this implementation, can be added to iterative MPI applications and requires as few as three lines of source code change. We verify our design for a particle dynamics application on desktop resources within a production commercial environment. 1.

Abstract — We propose a biologically inspired and fullydecentralized approach to the organization of computation that is based on the autonomous scheduling of strongly mobile agents on a peer-to-peer network. Our approach achieves the following design objectives: near-zero knowledge of network topology, zero knowledge of system status, autonomous scheduling, distributed computation, lack of specialized nodes. Every node is equally responsible for scheduling and computation, both of which are performed with practically no information about the system. We believe that this model is ideally suited for large-scale unstructured grids such as desktop grids. This model avoids the extensive system knowledge requirements of traditional Grid scheduling approaches. Contrary to the popular master/worker organization of current desktop grids, our approach does not rely on specialized super-servers or on application-specific clients. By encapsulating computation and scheduling behavior into mobile agents, we decouple both application code and scheduling functionality from the underlying infrastructure. The resulting system is one where every node can start a large grid job, and where the computation naturally organizes itself around available resources. Through the careful design of agent behavior, the resulting global organization of the computation can be customized for different classes of applications. In a previous paper, we described a proof-of-concept prototype for an independent task application. In this paper, we generalize the scheduling framework and demonstrate that our approach is applicable to a computation with a highly synchronous communication pattern, namely Cannon’s matrix multiplication. I.

Distributed Virtual Computer (DVC) is a computing environment which simplifies the development and execution of distributed applications on computational grids. DVC provides a simple set of abstractions to simplify application management of naming, security, communication, and resource, easing use of highly dynamic and heterogeneous resource environments. These abstractions enable complex collections of grid resources to be used in a fashion similar to private user or workgroup resources. The DVC model is attractive for lambda-grids with circuitswitched optical networks, providing a structure for exploiting unique communication and security properties. Examples of DVC’s include virtual clusters and virtual heterogeneous resource collections. We introduce the concept of a DVC, its system structure and mechanisms. We discuss the potential benefits of DVC’s for application programmers. 1.

Abstract. Computational Grids potentially offer cheap large-scale high-performance systems, but are a very challenging architecture, being heterogeneous, shared and hierarchical. Rather than requiring a programmer to explicitly manage this complex environment, we recommend using a high-level parallel functional language, like GpH, with largely automatic management of parallel coordination. We present GRID-GUM, an initial port of the distributed virtual shared-memory implementation of GpH for computational Grids. We show that, GRID-GUM delivers acceptable speedups on relatively low latency homogeneous and heterogeneous computational Grids. Moreover, we find that for heterogeneous computational Grids, load management limits performance. We present the initial design of GRID-GUM2, that incorporates new load management mechanisms that cheaply and effectively combine static and dynamic information to adapt to heterogeneous Grids. The mechanisms are evaluated by measuring four non-trivial programs with different parallel properties. The measurements show that the new mechanisms improve load distribution over the original implementation, reducing runtime by factors ranging from 17 % to 57%, and the greatest improvement is obtained for the most dynamic program.

The Model Coupling Toolkit (MCT) is a software library for constructing parallel coupled models from individual parallel models. MCT was created to address the challenges of creating a parallel coupler for the Community Climate System Model (CCSM). Each of the submodels that make up CCSM is a separate parallel application with its own domain decomposition, running on its own set of processors. This application contains multiple instances of the M × N problem, the problem of transferring data between two parallel programs running on disjoint sets of processors. CCSM also requires efficient data transfer to facilitate its interpolation algorithms. MCT was created as a generalized solution to handle these and other common functions in parallel coupled models. Here we describe