The simultaneous analysis of a number of related datasets using a single statistical model is an important problem in statistical computing. A parameterized statistical model is to be fitted on multiple datasets and tested for goodness of fit within a fixed analytical framework. Definitive conclusions are hopefully achieved by analyzing the datasets together. This paper proposes a strategy for the efficient execution of this type of analysis on heterogeneous clusters. Based on partitioning processors into groups for efficient communications and a dynamic loop scheduling approach for load balancing, the strategy addresses the variability of the computational loads of the datasets, as well as the unpredictable irregularities of the cluster environment. Results from preliminary tests of using this strategy to fit gamma-ray burst time profiles with vector functional coefficient autoregressive models on 64 processors of a general purpose Linux cluster demonstrate the effectiveness of the strategy.

Ioana Banicescu

Ricolindo L. Cari&#xf1;o

Jane L. Harvill

John Patrick Lestrade

Vector Nonlinear Time-Series Analysis of Gamma-Ray Burst Datasets on Heterogeneous Clusters

A new class of normalized explicit approximate inverse matrix techniques, based on normalized approximate factorization procedures, for solving sparse linear systems resulting from the finite difference discretization of partial differential equations in three space variables are introduced. A new parallel normalized explicit preconditioned conjugate gradient square method in conjunction with normalized approximate inverse matrix techniques for solving efficiently sparse linear systems on distributed memory systems, using Message Passing Interface (MPI) communication library, is also presented along with theoretical estimates on speedups and efficiency. The implementation and performance on a distributed memory MIMD machine, using Message Passing Interface (MPI) is also investigated. Applications on characteristic initial/boundary value problems in three dimensions are discussed and numerical results are given.

George A. Gravvanis

Konstantinos M. Giannoutakis

Parallel Preconditioned Conjugate Gradient Square Method Based on Normalized Approximate Inverses

Dan Nagle

High Performance Linux Clusters, by A. Joseph and D. Sloan

Administration of Grid resources is a time consuming and often tedious job. Most administrative requests are predictable, and in general, handling them requires knowledge of the local resources and the requester. In this paper we discuss a system to provide automated support for administrative requests, such as resource reservation and user account management. We propose using trust metrics to help judge the merits and suitability of each request. We outline how these metrics can be implemented using trust management techniques into a practical system we call GridAdmin.

Brian C. Clayton

Thomas B. Quillinan

Simon N. Foley

Automating Security Configuration for the Grid

The paper presents a performance model that can be used to optimally distribute computations over heterogeneous computers. This model is application-centric representing the speed of each computer by a function of the problem size. This way it takes into account the processor heterogeneity, the heterogeneity of memory structure, and the memory limitations at each level of memory hierarchy. A problem of optimal partitioning of an n-element set over p heterogeneous processors using this performance model is formulated, and its efficient solution of the complexity O(p3&#xd7; log2n) is given.

Alexey Lastovetsky

Ravi Reddy

Data Partitioning for Multiprocessors with Memory Heterogeneity and Memory Constraints

John Morrison

Keith Power

Philip Healy

Foreword

One of the important issues facing the development of the grid as a computational framework of the future is availability of grid-enabled software. In this context, we discuss possible approaches to constructing a grid-enabled version of a computer algebra system. Our case study involves Maple: the proposed Maple2g package allows the connection between Maple and the computational grids based on the Globus Toolkit. We present the design of the Maple2g package and follow with a detailed discussion of its implementation. Finally, we illustrate performance of Maple2g in a number of experiments.

Dana Petcu

Marcin Paprzycki

Diana Dubu

Design and Implementation of a Grid Extension for Maple

Agent oriented programming is often claimed to become the next breakthrough in development and implementation of large-scale complex software systems. At the same time it is rather difficult to find successful applications of agent technology, in particular when large-scale systems are considered. The aim of this paper is to investigate if one of the possible limits could be the scalability of existing agent environments. For this purpose we have selected JADE agent platform and investigated its performance in a number of test-scenarios. Results of our experiments are presented and discussed.

Krzysztof Chmiel

Maciej Gawinecki

Pawel Kaczmarek

Michal Szymczak

Efficiency of JADE Agent Platform

We describe how the R-GMA (Relational Grid Monitoring Architecture) can be used to allow for instrument monitoring in a Grid environment. The R-GMA has been developed within the European DataGrid Project (EDG) as a Grid Information and Monitoring System. It is based on the Grid Monitoring Architecture (GMA) from the Global Grid Forum (GGF), which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. It offers a global view of the information as if each Virtual Organisation had one large relational database. It provides a number of different Producer types with different characteristics; for example some support streaming of information. We describe the R-GMA component that allows for instrument monitoring, the CanonicalProducer. We also describe an example use of this approach in the European CrossGrid project, SANTA-G, a network monitoring tool.

Stuart Kenny

Brian Coghlan

David O'Callaghan

John Ryan

Rob Byrom

Laurence Field

Steve Hicks

Manish Soni

Antony Wilson

Xiaomei Zhu

Roney Cordenonsi

Ari Datta

Linda Cornwall

Abdeslem Djaoui

Norbert Podhorszki

The CanonicalProducer: An Instrument Monitoring Component of the Relational Grid Monitoring Architecture (R-GMA)

The aim of this paper is to present a qualitative evaluation of three state-of-the-art parallel languages: OpenMP, Unified Parallel C (UPC) and Co-Array Fortran (CAF). OpenMP and UPC are explicit parallel programming languages based on the ANSI standard. CAF is an implicit programming language. On the one hand, OpenMP designs for shared-memory architectures and extends the base-language by using compiler directives that annotate the original source-code. On the other hand, UPC and CAF designs for distribute-shared memory architectures and extends the base-language by new parallel constructs. We deconstruct each language into its basic components, show examples, make a detailed analysis, compare them, and finally draw some conclusions.

Ami Marowka

Execution Model of Three Parallel Languages: OpenMP, UPC and CAF

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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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