Steering in computational science: mesoscale modelling and simulation

This paper outlines the benefits of computational steering for high performance computing applications. Lattice-Boltzmann mesoscale fluid simulations of binary and ternary amphiphilic fluids in two and three dimensions are used to illustrate the substantial improvements which computational steering offers in terms of resource efficiency and time to discover new physics. We discuss details of our current steering implementations and describe their future outlook with the advent of computational grids.Comment: 40 pages, 11 figures. Accepted for publication in Contemporary Physic

Prospects for computational steering of evolutionary computation

Currently, evolutionary computation (EC) typically takes place in batch mode: algorithms are run autonomously, with the user providing little or no intervention or guidance. Although it is rarely possible to specify in advance, on the basis of EC theory, the optimal evolutionary algorithm for a particular problem, it seems likely that experienced EC practitioners possess considerable tacit knowledge of how evolutionary algorithms work. In situations such as this, computational steering (ongoing, informed user intervention in the execution of an otherwise autonomous computational process) has been profitably exploited to improve performance and generate insights into computational processes. In this short paper, prospects for the computational steering of evolutionary computation are assessed, and a prototype example of computational steering applied to a coevolutionary algorithm is presented

Computational steering

The traditional cycle in simulation is to prepare input, execute a simulation, and to visualize the results as a post-processing step. However, more insight and a higher productivity can be achieved if these activities are done simultaneously. This is the underlying idea of Computational Steering: researchers change parameters of their simulation on the fly and immediately receive feedback on the effect. In this paper the Computational Steering Environment, CSE, developed at CWI is described. We discuss the requirements of computational steering environment, its relation with high performance computing and networking, and show an application of its use

Computational Steering in the Problem Solving Environment WBCSim

Computational steering allows scientists to interactively control a numerical experiment and adjust parameters of the computation on-the-fly and explore “what if ” analysis. Computational steering effectively reduces computational time, makes research more efficient, and opens up new product design opportunities. There are several problem solving environments (PSEs) featuring computational steering. However, there is hardly any work explaining how to enable computational steering for PSEs embedded with legacy simulation codes. This paper describes a practical approach to implement computational steering for such PSEs by using WBCSim as an example. WBCSim is a Web based simulation system designed to increase the productivity of wood scientists conducting research on wood-based composites manufacturing processes. WBCSim serves as a prototypical example for the design, construction, and evaluation of small-scale PSEs. Various changes have been made to support computational steering across the three layers—client, server, developer—comprising the WBCSim system. A detailed description of the WBCSim system architecture is presented, along with a typical scenario of computational steering usage

Computational Steering of Cluster Formation in Brownian Suspensions

We simulate cluster formation of model colloidal particles interacting via DLVO (Derjaguin, Landau, Vervey, Overbeek) potentials. The interaction potentials can be related to experimental conditions, defined by the pH-value, the salt concentration and the volume fraction of solid particles suspended in water. The system shows different structural properties for different conditions, including cluster formation, a glass-like repulsive structure, or a liquid suspension. Since many simulations are needed to explore the whole parameter space, when investigating the properties of the suspension depending on the experimental conditions, we have developed a steering approach to control a running simulation and to detect interesting transitions from one region in the configuration space to another. The advantages of the steering approach and the restrictions of its applicability due to physical constraints are illustrated by several example cases.Comment: 9 pages, 4 figures, submitted to Proceedings of the Fourth International Conference on Mesoscopic Methods in Engineering and Science (ICMMES) 2007 (Munich, Germany), revised version, 2 figures exchanged, some parts rephrase

Large-scale grid-enabled lattice-Boltzmann simulations of complex fluid flow in porous media and under shear

Well designed lattice-Boltzmann codes exploit the essentially embarrassingly parallel features of the algorithm and so can be run with considerable efficiency on modern supercomputers. Such scalable codes permit us to simulate the behaviour of increasingly large quantities of complex condensed matter systems. In the present paper, we present some preliminary results on the large scale three-dimensional lattice-Boltzmann simulation of binary immiscible fluid flows through a porous medium derived from digitised x-ray microtomographic data of Bentheimer sandstone, and from the study of the same fluids under shear. Simulations on such scales can benefit considerably from the use of computational steering and we describe our implementation of steering within the lattice-Boltzmann code, called LB3D, making use of the RealityGrid steering library. Our large scale simulations benefit from the new concept of capability computing, designed to prioritise the execution of big jobs on major supercomputing resources. The advent of persistent computational grids promises to provide an optimal environment in which to deploy these mesoscale simulation methods, which can exploit the distributed nature of compute, visualisation and storage resources to reach scientific results rapidly; we discuss our work on the grid-enablement of lattice-Boltzmann methods in this context.Comment: 17 pages, 6 figures, accepted for publication in Phil.Trans.R.Soc.Lond.

Computational steering in realitygrid

The RealityGrid project (http://www.realitygrid.org) aims both to enable the discovery of new materials through integrated experiments and to understand the behaviour of physical systems based on the properties of their microscopic components using diverse simulation methods spanning many time and length scales. A central theme of RealityGrid is the facilitation of distributed and collaborative steering of parallel simulation codes and simultaneous on-line, high-end visualisation. In this paper, we review the motivations for computational steering and introduce the RealityGrid steering library and associated software. We then outline the capabilities of the library and describe the service-oriented architecture of the latest implementation, in which the steering controls of the application are exposed through an OGSI-compliant Grid service

A survey of computational steering environments

Computational steering is a powerful concept that allows scientists to interactively control a computational process during its execution. In this paper, a survey of computational steering environments for the on-line steering of ongoing scientific and engineering simulations is presented. These environments can be used to create steerable applications for model exploration, algorithm experimentation, or performance optimization. For each environment the scope is identified, the architecture is summarized, and the concepts of the user interface is described. The environments are compared and conclusions and future research issues are given

Interactive simulation and visualization

Journal ArticleMost of us perform data analysis and visualization only after everything else is finished, which often means that we don't discover errors invalidating the results of our simulation until postprocessing. A better approach would be to improve the integration of simulation and visualization into the entire process so that you can make adjustments along the way. We call this approach computational steering. Computational steering is the capacity to control all aspects of the computational science pipeline-the succession of steps required to solve computational science and engineering problems. When you interactively explore a simulation in time and space, you steer it. In this sense, you can rely on steering to assist in debugging and to modify the computational aspects of your application