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Antonioletti, Mario; Atkinson, Malcolm; Baxter, Rob; Borley, Andrew; Hong, Neil Chue; Collins, Brian; Hardman, Neil; Hume, Alastair; Knox, Alan J.; Jackson, Mike; Krause, Amy; Laws, Simon M.; Magowan, James; Paton, Norman W.; Pearson, Dave; Sugden, Tom; Watson, Paul; Westhead, Martin

doi:10.1002/cpe.v17:2/4

Cited by 157 publications

(21 citation statements)

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“…In such a situation, resource brokers that use such a scheduling mechanism are reduced to ineffective random allocation. Contrary to this, however, what works well in such situations is the Condor glide-in approach (Thain et al 2005). A glide-in is a self-installing Condor execution service that may be submitted to a remote resource.…”

Section: (B ) the Rtgrid Resource Brokermentioning

confidence: 99%

High-performance computing for Monte Carlo radiotherapy calculations

Downes

Yaikhom

Giddy

et al. 2009

Phil. Trans. R. Soc. A.

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We report on the RTGrid project, which investigates approaches for using highperformance computing infrastructures, such as the grid, in order to reduce the turnaround time of Monte Carlo (MC) simulation-based radiotherapy treatment planning. The main aim of this project is to render accurate dose calculations using MC simulations clinically feasible. To this end, we have successfully implemented and deployed the RTGrid distributed simulation framework for MC dose calculations. In this paper, we present the main experimental findings.

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Section: (B ) the Rtgrid Resource Brokermentioning

confidence: 99%

High-performance computing for Monte Carlo radiotherapy calculations

Downes

Yaikhom

Giddy

et al. 2009

Phil. Trans. R. Soc. A.

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“…HTCondor [23] supports advanced GPGPU resource publication, service-discovery, per-job GPGPU match-making, and job-management [24]. Indeed, HTCondor is central to the the UMD WMS match-making service.…”

Section: Htcondor Gpgpu Supportmentioning

confidence: 99%

Supporting job-level secure access to GPGPU resources on existing grid infrastructures

Walsh¹,

Dukes²

2014

Annals of Computer Science and Information Systems

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Abstract-Grids provide secure, utility-like access to a wide variety of large-scale, distributed computational and storage resources. In particular, the European Grid Infrastructure (EGI) and Open Science Grid (OSG) have excelled in processing vast workloads of independent jobs for the research community.Researchers demand increasingly faster processing speeds to solve increasingly larger and more complex problems. To meet this need, attention has shifted over the past decade away from single-core processing models towards the use of multi-core, many-core and massively parallel computational accelerators. The increasing availability and use of General Purpose Graphic Processing Units (GPGPUs) are an example of this.This paper addresses many of the challenges that exist in the integration of resources such as GPGPUs into Grid infrastructures. Specifically, solutions are proposed for discovering and describing GPGPU Grid resources, specifying multi-GPGPU job requirements, performing multi-GPGPU allocation to jobs, dynamically updating publicly-readable GPGPU usage information and enforcing GPGPU access control to prevent distinct jobs from inadvertently accessing the same device. The proposed solution is fully compatible with widely-used and accepted standards and middleware including the GLUE 2.0 schema and EGI Unified Middleware Distribution. A prototype implementation is also described.

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“…BLAH, in turn, interacts with the client-side LRMS environment, which might consist of a set of command line tools which interact with the server-side LRMS. At the time of writing BLAH supports LSF, PBS/Torque, and Condor [20]; support for Sun Grid Engine (SGE) is currently being implemented as well. It is also possible to create other, ad-hoc connectors to interact with other types of batch systems.…”

Section: Deployment Layoutmentioning

confidence: 99%

Design and implementation of the gLite CREAM job management service

Aiftimiei

Andreetto

Bertocco

et al. 2010

Future Generation Computer Systems

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Job execution and management is one of the most important functionality provided by every modern Grid systems. In this paper we describe how the problem of job management has been addressed in the gLite middleware by means of the CREAM and CEMonitor services. CREAM (Computing Resource Execution and Management) provides a job execution and management capability for Grids, while CEMonitor is a general-purpose asynchronous event notification framework. Both components expose a Web Service interface allowing conforming clients to submit, manage and monitor computational jobs to a Local Resource Management System.

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Cited by 157 publications

References 0 publications

High-performance computing for Monte Carlo radiotherapy calculations

High-performance computing for Monte Carlo radiotherapy calculations

Supporting job-level secure access to GPGPU resources on existing grid infrastructures

Design and implementation of the gLite CREAM job management service

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