Parallel and Distributed System Simulation

This exploratory study initiated our research into the software infrastructure necessary to support the modeling and simulation techniques that are most appropriate for the Information Power Grid. Such computational power grids will use high-performance networking to connect hardware, software, instruments, databases, and people into a seamless web that supports a new generation of computation-rich problem solving environments for scientists and engineers. In this context we looked at evaluating the NetSolve software environment for network computing that leverages the potential of such systems while addressing their complexities. NetSolve's main purpose is to enable the creation of complex applications that harness the immense power of the grid, yet are simple to use and easy to deploy. NetSolve uses a modular, client-agent-server architecture to create a system that is very easy to use. Moreover, it is designed to be highly composable in that it readily permits new resources to be added by anyone willing to do so. In these respects NetSolve is to the Grid what the World Wide Web is to the Internet. But like the Web, the design that makes these wonderful features possible can also impose significant limitations on the performance and robustness of a NetSolve system. This project explored the design innovations that push the performance and robustness of the NetSolve paradigm as far as possible without sacrificing the Web-like ease of use and composability that make it so powerful

DIET : new developments and recent results

Among existing grid middleware approaches, one simple, powerful, and flexibleapproach consists of using servers available in different administrative domainsthrough the classic client-server or Remote Procedure Call (RPC) paradigm.Network Enabled Servers (NES) implement this model also called GridRPC.Clients submit computation requests to a scheduler whose goal is to find aserver available on the grid. The aim of this paper is to give an overview of anNES middleware developed in the GRAAL team called DIET and to describerecent developments. DIET (Distributed Interactive Engineering Toolbox) is ahierarchical set of components used for the development of applications basedon computational servers on the grid.Parmi les intergiciels de grilles existants, une approche simple, flexible et performante consiste a utiliser des serveurs disponibles dans des domaines administratifs différents à travers le paradigme classique de l’appel de procédure àdistance (RPC). Les environnements de ce type, connus sous le terme de Network Enabled Servers, implémentent ce modèle appelé GridRPC. Des clientssoumettent des requêtes de calcul à un ordonnanceur dont le but consiste àtrouver un serveur disponible sur la grille.Le but de cet article est de donner un tour d’horizon d’un intergiciel développédans le projet GRAAL appelé DIET 1. DIET (Distributed Interactive Engineering Toolbox) est un ensemble hiérarchique de composants utilisés pour ledéveloppement d’applications basées sur des serveurs de calcul sur la grille

Use of A Network Enabled Server System for a Sparse Linear Algebra Grid Application

Solving systems of linear equations is one of the key operations in linear algebra. Many different algorithms are available in that purpose. These algorithms require a very accurate tuning to minimise runtime and memory consumption. The TLSE project provides, on one hand, a scenario-driven expert site to help users choose the right algorithm according to their problem and tune accurately this algorithm, and, on the other hand, a test-bed for experts in order to compare algorithms and define scenarios for the expert site. Both features require to run the available solvers a large number of times with many different values for the control parameters (and maybe with many different architectures). Currently, only the grid can provide enough computing power for this kind of application. The DIET middleware is the GRID backbone for TLSE. It manages the solver services and their scheduling in a scalable way.La résolution de systèmes linéaires creux est une opération clé en algèbre linéaire. Beaucoup d’algorithmes sont utilisés pour cela, qui dépendent de nombreux paramètres, afin d’offrir une robustesse, une performance et une consommation mémoire optimales. Le projet GRID-TLSE fournit d’une part, un site d’expertise basé sur l’utilisation de scénarios pour aider les utilisateurs à choisir l’algorithme qui convient le mieux à leur problème ainsi que les paramètres associés; et d’autre part, un environnement pour les experts du domaine leur permettant de comparer efficacement des algorithmes et de définir dynamiquement de nouveaux scénarios d’utilisation. Ces fonctionnalités nécessitent de pouvoir exécuter les logiciels de résolution disponibles un grand nombre de fois,avec beaucoup de valeurs différentes des paramètres de contrôle (et éventuellement sur plusieurs architectures de machines). Actuellement, seule la grille peut fournir la puissance de calcul pour ce type d’applications. L’intergiciel DIETest utilisé pour gérer la grille, les différents services, et leur ordonnancement efficace

Thermal Evolution of Planetesimals and Protoplanets in the Terrestrial Planet Region: Code Optimization and Implementation on a Distributed Grid using NetSolve

A code for asteroidal heat transfer and growth is optimized for performance. The Gauss elimination routine for the solver is replaced by a sparse matrix routine. Finite element matrix assembly operations are rewritten to reduce operations involving 3D arrays to 1D. Advantage is taken of the sparse matrix structure of finite element matrices in reducing 2D arrays to 1D. The number of vector touches are reduced to the extent possible, by carrying over statements from one iteration to the next. The number of do loops are reduced by merging several do loops into one. The optimization reduced the CPU time taken to run the code from 297 sec to 0.88 sec for a matrix size of 100, an improvement of 99.70%. More importantly, the algorithm was reduced from a O(n3) operation to a O(n) operation. Thus, the percent time difference between the optimized and unoptimized versions is greater at larger matrix sizes. At matrix sizes of 100, the number of floating point operations were reduced from 2.39 E+09 to 2.99E+07, an improvement of 98.75% and the performance was increased by about 4 times, from 8.06 MFLOPS/s to 33.92 MFLOPS/s. Because of inefficiency in memory allocation, the maximum matrix size for the unoptimized code was limited to 200. This was increased to 5,000,000 for the optimized code. A version of the code was implemented on NetSolve and added to the list of problems on netsolve.cs.utk.edu. Two sample movies were generated using OpenGL to explain the scientific significance of the code. With the implementation of the optimized code, applications to address scientific problems can now be envisioned that were previously thought to be prohibitive in terms of computer time

The Java CoG kit grid desktop : a simple and central approach to grid computing using the graphical desktop paradigm.

Grid computing is evolving as a service based, flexible and secure resource sharing environment. Currently, with the help of Grid middleware toolkits, Grids are exposing their services through programming models and command line interfaces, requiring much technical knowledge of the backend Grid systems. Grid portals also exist, but fall short on integrating with native environments and maintaining a uniform user interface from portal to portal. In order to gain wider acceptance within the large and less technical oriented user communities, we need a homogeneous graphical user environment that supports the challenging task of providing Grid users an easy to use, seamless and transparent interface requiring minimal user participation. Motivated by the needs of these users, we are presenting the Grid Desktop based on the popularity of the graphical desktop paradigms such as KDE and Windows XP. The Java CoG Kit Grid Desktop is a user centric workspace that enhances the normal operating system desktop paradigm by interlacing Grid concepts and leveraging commodity technologies like Java. The Grid Desktop contributes to the Java CoG Kit architecture and delivers ubiquitous computing through the Java CoG Kit abstractions, portability through XML and Java Web start technologies, and a simple user interface by following the vastly popular desktop patterns such as drag-n-drop

MPICH-G2: A Grid-Enabled Implementation of the Message Passing Interface

Application development for distributed computing "Grids" can benefit from tools that variously hide or enable application-level management of critical aspects of the heterogeneous environment. As part of an investigation of these issues, we have developed MPICH-G2, a Grid-enabled implementation of the Message Passing Interface (MPI) that allows a user to run MPI programs across multiple computers, at the same or different sites, using the same commands that would be used on a parallel computer. This library extends the Argonne MPICH implementation of MPI to use services provided by the Globus Toolkit for authentication, authorization, resource allocation, executable staging, and I/O, as well as for process creation, monitoring, and control. Various performance-critical operations, including startup and collective operations, are configured to exploit network topology information. The library also exploits MPI constructs for performance management; for example, the MPI communicator construct is used for application-level discovery of, and adaptation to, both network topology and network quality-of-service mechanisms. We describe the MPICH-G2 design and implementation, present performance results, and review application experiences, including record-setting distributed simulations.Comment: 20 pages, 8 figure

Grid computing in image analysis

Diagnostic surgical pathology or tissue–based diagnosis still remains the most reliable and specific diagnostic medical procedure. The development of whole slide scanners permits the creation of virtual slides and to work on so-called virtual microscopes. In addition to interactive work on virtual slides approaches have been reported that introduce automated virtual microscopy, which is composed of several tools focusing on quite different tasks. These include evaluation of image quality and image standardization, analysis of potential useful thresholds for object detection and identification (segmentation), dynamic segmentation procedures, adjustable magnification to optimize feature extraction, and texture analysis including image transformation and evaluation of elementary primitives

Co-citation analysis of literature in e-science and e-infrastructures

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordAdvances in computer networking, storage technologies and high-performance computing are helping global communities of researchers to address increasingly ambitious problems in Science collaboratively. EScience is the “science of this age”; it is realized through collaborative scientific enquiry which requires the utilization of non-trivial amounts of computing resources and massive data sets. Core to this is the integrated set of technologies collectively known as e-Infrastructures. In this paper, we explore the e-Science and the eInfrastructure knowledge base through co-citation analysis of existing literature. The dataset for this analysis is downloaded from the ISI Web of Science and includes over 12,000 articles. We identify prominent articles, authors and articles with citation bursts. The detection of research clusters and the underlying seminal papers provide further insights. Our analysis is an important source of reference for academics, researchers and students starting research in this field

On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

International audienceScientific research is becoming increasingly dependent on the large-scale analysis of data using distributed computing infrastructures (Grid, cloud, GPU, etc.). Scientific computing (Petitet et al. 1999) aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering. In this paper, we describe the services of an integrated portal based on the P-Grade (Parallel Grid Run-time and Application Development Environment) portal (http://www.p-grade.hu) that enables the solution of large-scale linear systems of equations using direct solvers, makes easier the use of parallel block iterative algorithm and provides an interface for parallel decision making algorithms. The ultimate goal is to develop a single sign on integrated multi-service environment providing an easy access to different kind of mathematical calculations and algorithms to be performed on hybrid distributed computing infrastructures combining the benefits of large clusters, Grid or cloud, when needed