Steering in computational science: Mesoscale modelling and simulation

Chin, Jonathan; Harting, Jens; Jha, Shantenu; Coveney, PV; Porter, Andrew; Pickles, SM

doi:10.1080/00107510310001605046

Cited by 42 publications

(36 citation statements)

References 62 publications

(86 reference statements)

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“…To overcome this problem, they implemented an environment in which a data manager facilitates the interaction between user applications and steering components. Chin et al [15] incorporated computational steering in mesoscale lattice Boltzmann simulations and showed the benefits of their work. They discussed that large scale simulations require not only computational resources but tools to manage these simulations and their produced results, what they called simulation-analysis loop.…”

Section: Related Workmentioning

confidence: 99%

JobPruner: A machine learning assistant for exploring parameter spaces in HPC applications

Silva

Netto

Cunha

2018

Future Generation Computer Systems

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Abstract-High Performance Computing (HPC) applications are essential for scientists and engineers to create and understand models and their properties. These professionals depend on the execution of large sets of computational jobs that explore combinations of parameter values. Avoiding the execution of unnecessary jobs brings not only speed to these experiments, but also reductions in infrastructure usageparticularly important due to the shift of these applications to HPC cloud platforms. Our hypothesis is that data generated by these experiments can help users in identifying such jobs. To address this hypothesis we need to understand the similarity levels among multiple experiments necessary for job elimination decisions and the steps required to automate this process. In this paper we present a study and a machine learning-based tool called JobPruner to support parameter exploration in HPC experiments. The tool was evaluated with three real-world use cases from different domains including seismic analysis and agronomy. We observed the tool reduced 93% of jobs in a single experiment, while improving quality in most scenarios. In addition, reduction in job executions was possible even considering past experiments with low correlations.

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Section: Related Workmentioning

confidence: 99%

JobPruner: A machine learning assistant for exploring parameter spaces in HPC applications

Silva

Netto

Cunha

2018

Future Generation Computer Systems

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“…Before we were able to take advantage of computational steering techniques, our work usually involved large-scale parameter searches organized as taskfarming jobs, in order to find the areas of interest of the available parameter space. The technique of computational steering (Brooke et al 2003;Chin et al 2003;Love et al 2003) has been used successfully in smaller-scale simulations to optimize resource usage. Typically, the procedure for running a simulation of the self-assembly of a mesophase would be to set up the initial conditions, and then submit a batch job to run for a certain, fixed number of time-steps.…”

Section: Technical Projectsmentioning

confidence: 99%

Large-scale lattice Boltzmann simulations of complex fluids: advances through the advent of computational Grids

Harting

Chin

Venturoli

et al. 2005

Phil. Trans. R. Soc. A.

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Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. During the last 2.5 years, the RealityGrid project has allowed us to be one of the few scientific groups involved in the development of computational Grids. Since smoothly working production Grids are not yet available, we have been able to substantially influence the direction of software and Grid deployment within the project. In this paper, we review our results from large-scale three-dimensional lattice Boltzmann simulations performed over the last 2.5 years. We describe how the proactive use of computational steering, and advanced job migration and visualization techniques enabled us to do our scientific work more efficiently. The projects reported on in this paper are studies of complex fluid flows under shear or in porous media, as well as large-scale parameter searches, and studies of the self-organization of liquid cubic mesophases.

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“…[17,20]) spanning many time and length scales and the discovery of new materials through integrated experiments. A central theme of RealityGrid is the facilitation of distributed and collaborative exploration of parameter space through computational steering and on-line, high-end visualization.…”

Section: Aims Of Realitygridmentioning

confidence: 99%

Application Steering in a Collaborative Environment

Brooke

Eickermann

Woessner

2003

Proceedings of the 2003 ACM/IEEE Conference on Supercomputing

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In this showcase we will present live running simulations which are integrated into the Access Grid in a variety of different ways. An example of this is the use of vncto distribute a desktop on which the simulation is being displayed. Another example is the redirection of the visualization into vic to make 3D animations available over the Access Grid. Other examples that will be explored are the use of SGI's OpenGL VizServer to direct the output of a graphics supercomputer located on the Grid to the AG locations. We will also utilize the ability of the next generation AG software to directly link with visualization toolkits such as vtk, AVS/Express, or COVISE as an integrated part of the Virtual Venue as this functionality has developed by the time of the SC2003 demonstrations. We also demonstrate steering in a collaborative setting using a steering service which is fully compliant with OGSI and with the proposed OGSA architecture. This can be integrated with current Grid middleware (e.g. GT2 and UNICORE) using a specially developed Perl hosting environment, OGSI:Lite.

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Steering in computational science: Mesoscale modelling and simulation

Cited by 42 publications

References 62 publications

JobPruner: A machine learning assistant for exploring parameter spaces in HPC applications

JobPruner: A machine learning assistant for exploring parameter spaces in HPC applications

Large-scale lattice Boltzmann simulations of complex fluids: advances through the advent of computational Grids

Application Steering in a Collaborative Environment

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