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Computational steering refers to the real-time interaction of a scientist with their running simulation code. Despite the many benefits associated with computational steering, its uptake to date has been limited. In this paper we discuss the reasons for this and how the computational steering library and associated tools developed as part of the RealityGrid project aim to tackle them. We describe the functionality of the steering library and the use of Grid services in constructing a generic, dynamic architecture for discovering, steering and connecting visualization software to running simulations. The use of on-line visualization for providing feedback to the scientist is described, including the ways in which it may be enhanced through tools such as Chromium and Access Grid. Finally, we illustrate the flexibility of our approach by describing the functionality that has been added to various simulation codes as part of the RealityGrid project.

The UNICORE (UNiform Interface to COmputing REsources) software provides a Grid infrastructure together with a computing portal for engineers and scientists to access supercomputer centres from anywhere on the Internet. While UNICORE is primarily designed for the submission and control of batch jobs, it is also feasible to establish an on-line connection between an application and the UNICORE user-client. This opens up the possibility of performing on-line visualization and computational steering of applications under UNICORE control while maintaining the security provided by this system. This contribution describes the design of a steering extension to UNICORE based on the steering toolkit VISIT (VISualization Interface Toolkit). VISIT is a lightweight library that supports bidirectional data exchange between visualizations and parallel applications. As an example application, a parallel simulation of a laser-plasma interaction that can be steered by an AVS/Express application is presented.

The grid has the potential to transform collaborative scientific investigations through the use of closely coupled computational and visualization resources, which may be geographically distributed, in order to harness greater power than is available at a single site. Scientific applications to benefit from the grid include visualization, computational science, environmental modelling and medical imaging. Unfortunately, the diversity, scale and location of the required resources can present a dilemma for the scientific worker because of the complexity of the underlying technology. As the scale of the scientific problem under investigation increases so does the nature of the scientist's interaction with the supporting infrastructure. The increased distribution of people and resources within a grid-based environment can make resource sharing and collaborative interaction a critical factor to their success. Unless the technological barriers affecting user accessibility are reduced, there is a danger that the only scientists to benefit will be those with reasonably high levels of computer literacy. This paper examines a number of important human factors of user interaction with the grid and expresses this in the context of the science undertaken by RealityGrid, a project funded by the UK e-Science programme. Critical user interaction issues will also be highlighted by comparing grid computational steering with supervisory control systems for local and remote access to the scientific environment. Finally, implications for future grid developers will be discussed with a particular emphasis on how to improve the scientists' access to what will be an increasingly important resource.

The grid has been developed to support large-scale computer simulations in a diverse range of scientific and engineering fields. Consequently, the increasing availability of powerful distributed computing resources is changing how scientists undertake large-scale modelling/simulation. Instead of being limited to local computing resources, scientists are now able to make use of supercomputing facilities around the world. These grid resources comprise specialized distributed three-dimensional visualization environments through to massive computational systems. The scientist usually accesses these resources from reasonably high-end desktop computers. Even though most modern desktop computers are provided with reasonably powerful three-dimensional graphical hardware, not all scientific applications require high-end three-dimensional visualization because the data of interest is essentially numerical or two-dimensional graphical data. For these applications, a much simpler two-dimensional graphical displays can be used. Since large jobs can take many hours to complete the scientist needs access to a technology that will allow them to still monitor and control their job while away from their desks. This paper describes an effective method of monitoring and controlling a set of chained computer simulations by means of a lightweight steering client based on a small personal digital assistant (PDA). The concept of using a PDA to steer a series of computational jobs across a supercomputing resource may seem strange at first but when scientists realize they can use these devices to connect to their computation wherever there is a wireless network (or cellular phone network) the concept becomes very compelling. Apart from providing a much needed easy-to-use interface, the PDA-based steering client has the benefit of freeing the scientist from the desktop. It is during this monitoring stage that the hand-held PDA client is of particular value as it gives the application scientist greater freedom to leave his or her desk but still communicate with their simulation, with the proviso that they remain within the range of a wireless network.

There are two main avenues to design space exploration. In the first approach, a simulation is run, analyzed, the problem modified, and the simulation run again. In the second approach, an ensemble simulation is performed and the battery of results is leveraged to construct a surrogate model for a given quantity of interest (QoI). The first approach allows a practitioner to methodically move through the design space and analyze a solution field. A disadvantage of this technique is that each new simulation requires time-consuming setup. The second approach provides the practitioner with a global view of the problem, but requires a priori design space limits and the QoI specification. In this work, we introduce an immersive simulation software framework that enables practitioners to maintain the flexibility of the first approach, while eliminating the burden of setting up new simulations. Immersive simulation can also be used to inform the second approach, establishing limits and clarifying QoI selection prior to the launch of an ensemble simulation. We demonstrate live, reconfigurable visualization of on-going simulations coupled with live, reconfigurable problem definition that guides users in determining problem parameters. Ultimately, an immersive simulation framework enables more efficient design space exploration that reduces the gap between simulations, data analysis, and insight extraction.Article HighlightsIntroduce and demonstrate an immersive simulation software framework that enables in situ visualization and problem redefinition.Efficient quantity of interest extraction that reduces disk storage by a factor of 50,000.Demonstrate the compatibility of immersive simulation with ensemble simulation and sensitivity analysis.

The development of supercomputers and multi-scale computational fluid dynamics (CFD) models based on adaptive mesh refinement (AMR) enabled fast, large-scale, and high fidelity CFD simulations. Interactive in situ steering is an effective tool for debugging, searching for optimal solutions, and analyzing inverse problems in such CFD simulations. We propose an interactive in situ steering framework for large-scale CFD simulations on GPU supercomputers. This framework employs in situ particle-based volume rendering (PBVR), in situ data sampling, and a file-based control that enables interactive and asynchronous communication of steering parameters, compressed visualization particle data, and sampled monitoring data between supercomputers and user PCs. The parallelized PBVR is processed on the host CPU to avoid interference with CFD simulations on the GPU. We apply the proposed framework to a real-time plume dispersion analysis code CityLBM, which computes the lattice Boltzmann method on the block AMR grid using GPU supercomputers. In the numerical experiment, we address an inverse problem to find a pollutant source from the observation data at monitoring points and demonstrate the effectiveness of the human-in-the-loop approach via the in situ steering framework. Graphical abstract

The increase of computational resources with the generalization of massively parallel supercomputers benefits to various fields of physics among which turbulence and fluid mechanics, making it possible to increase time and space accuracy and gain further knowledge in fundamental mechanisms. Parametric studies, high fidelity statistics, high resolutions, can be realized. However, this access poses many problems in terms of data management, analysis and visualization. Traditional workflow, consisting of writing raw data on disks and performing post-processing to extract physical quantities of interest, considerably slows down the analysis, if not becomes impossible, because of data transfer, storage and re-accessibility issues. This is particularly difficult when it comes to visualization. Usage has to be revisited to maintain consistency with the accuracy of the computation step and in this context, in situ processing is a promising approach. We developed an in situ analysis and visualization strategy with an hybrid method for transitional and turbulent flow analysis with a pseudo-spectral solver. It is shown to have a low impact on computational time with a reasonable increase of resource usage, while enriching data exploration. Large time sequences have been analyzed. This could not have been achieved with the traditional workflow. Moreover, computational steering has been performed with real-time adjustment of the simulations, thereby getting closer to a numerical experiment process.

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.

Simulation of complex phenomena is usually a long computing process and it has been traditionally performed in batch mode on large high performance computing (HPC) systems. However, advances in computer processing and networking capabilities can now be used to monitor and alter simulation parameters whilst it is running. This process is called computational steering. By combining this capability with advanced communication tools, it is now possible for a group of scientists located across the world to work collaboratively while visualising on-going simulations. This raise the possibility that researches can now share their experience and promote new ideas and solutions by exploring collaboratively the solution space of a complex simulation. In this paper, a collaborative computational steering environment specialised to solve CFD problems is presented.

A framework for accelerating modern long-running astrophysical simulations is presented, which is based on a hierarchical architecture where computational steering in the high-resolution run is performed under the guide of knowledge obtained in the gradually refined ensemble analyses. Several visualization schemes for facilitating ensemble management, error analysis, parameter grouping and tuning are also integrated owing to the pluggable modular design. The proposed approach is prototyped based on the Flash code, and it can be extended by introducing userdefined visualization for specific requirements. Two real-world simulations, i.e., stellar wind and supernova remnant, are carried out to verify the proposed approach.