Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry

Speeding-up the execution of credit risk simulations using desktop grid computing: A case study

This paper describes a case study that was undertaken at a leading European Investment bank in which desktop grid computing was used to speed-up the execution of Monte Carlo credit risk simulations. The credit risk simulations were modelled using commercial-off-the-shelf simulation packages (CSPs). The CSPs did not incorporate built-in support for desktop grids, and therefore the authors implemented a middleware for desktop grid computing, called WinGrid, and interfaced it with the CSP. The performance results show that WinGrid can speed-up the execution of CSP-based Monte Carlo simulations. However, since WinGrid was installed on non-dedicated PCs, the speed-up achieved varied according to users’ PC usage. Finally, the paper presents some lessons learnt from this case study. It is expected that this paper will encourage simulation practitioners and CSP vendors to experiment with desktop grid computing technologies with the objective of speeding-up simulation experimentation

Investigating grid computing technologies for use with commercial simulation packages

As simulation experimentation in industry become more computationally demanding, grid computing can be seen as a promising technology that has the potential to bind together the computational resources needed to quickly execute such simulations. To investigate how this might be possible, this paper reviews the grid technologies that can be used together with commercial-off-the-shelf simulation packages (CSPs) used in industry. The paper identifies two specific forms of grid computing (Public Resource Computing and Enterprise-wide Desktop Grid Computing) and the middleware associated with them (BOINC and Condor) as being suitable for grid-enabling existing CSPs. It further proposes three different CSP-grid integration approaches and identifies one of them to be the most appropriate. It is hoped that this research will encourage simulation practitioners to consider grid computing as a technologically viable means of executing CSP-based experiments faster

Leveraging simulation practice in industry through use of desktop grid middleware

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources)

Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

BRAHMS: Novel middleware for integrated systems computation

Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological. At the same time, the rise of the philosophy of embodied modelling is generating a need to deploy biological models as controllers for robots in real-world environments. Finally, robotics engineers are beginning to find value in seconding biomimetic control strategies for use on practical robots. Together with the ubiquitous desire to make good on past software development effort, these trends are throwing up new challenges of intellectual and technological integration (for example across scales, across disciplines, and even across time) - challenges that are unmet by existing software frameworks. Here, we outline these challenges in detail, and go on to describe a newly developed software framework, BRAHMS. that meets them. BRAHMS is a tool for integrating computational process modules into a viable, computable system: its generality and flexibility facilitate integration across barriers, such as those described above, in a coherent and effective way. We go on to describe several cases where BRAHMS has been successfully deployed in practical situations. We also show excellent performance in comparison with a monolithic development approach. Additional benefits of developing in the framework include source code self-documentation, automatic coarse-grained parallelisation, cross-language integration, data logging, performance monitoring, and will include dynamic load-balancing and 'pause and continue' execution. BRAHMS is built on the nascent, and similarly general purpose, model markup language, SystemML. This will, in future, also facilitate repeatability and accountability (same answers ten years from now), transparent automatic software distribution, and interfacing with other SystemML tools. (C) 2009 Elsevier Ltd. All rights reserved

Cost-effective HPC clustering for computer vision applications

We will present a cost-effective and flexible realization of high performance computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU parallel Knoppix package with message passing interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB

A grid computing framework for commercial simulation packages

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.An increased need for collaborative research among different organizations, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users non-trivial access to geographically dispersed computing resources (processors, storage, applications, data, instruments, etc.) that are administered in multiple computer domains. The term grid computing or grids is popularly used to refer to such distributed systems. A broader definition of grid computing includes the use of computing resources within an organization for running organization-specific applications. This research is in the context of using grid computing within an enterprise to maximize the use of available hardware and software resources for processing enterprise applications. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by simulation practitioners using Windows-based commercially available simulation packages to model simulations in industry. These packages are commonly referred to as Commercial Off-The-Shelf (COTS) Simulation Packages (CSPs). The study identifies several higher level grid services that could be potentially used to support the practise of simulation in industry. It proposes a grid computing framework to investigate these services in the context of CSP-based simulations. This framework is called the CSP-Grid Computing (CSP-GC) Framework. Each identified higher level grid service in this framework is referred to as a CSP-specific service. A total of six case studies are presented to experimentally evaluate how grid computing technologies can be used together with unmodified simulation packages to support some of the CSP-specific services. The contribution of this thesis is the CSP-GC framework that identifies how simulation practise in industry may benefit from the use of grid technology. A further contribution is the recognition of specific grid computing software (grid middleware) that can possibly be used together with existing CSPs to provide grid support. With its focus on end-users and end-user tools, it is intended that this research will encourage wider adoption of grid computing in the workplace and that simulation users will derive benefit from using this technology

Leveraging HTC for UK eScience with very large Condor pools: demand for transforming untapped power into results

We provide an insight into the demand from the UK eScience community for very large HighThroughput Computing resources and provide an example of such a resource in current productionuse: the 930-node eMinerals Condor pool at UCL. We demonstrate the significant benefits thisresource has provided to UK eScientists via quickly and easily realising results throughout a rangeof problem areas. We demonstrate the value added by the pool to UCL I.S infrastructure andprovide a case for the expansion of very large Condor resources within the UK eScience Gridinfrastructure. We provide examples of the technical and administrative difficulties faced whenscaling up to institutional Condor pools, and propose the introduction of a UK Condor/HTCworking group to co-ordinate the mid to long term UK eScience Condor development, deploymentand support requirements, starting with the inaugural UK Condor Week in October 2004