Computational steering and the SCIRun integrated problem solving environment

Journal ArticleSCIRun is a problem solving environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. We review related systems and introduce a taxonomy that explores different computational steering solutions. Considering these approaches, we discuss why a tightly integrated problem solving environment, such as SCIRun, simplifies the design and debugging phases of computational science applications and how such an environment aids in the scientific discovery process

Integrated problem solving environment: the SCIRun computational steering system

Journal ArticleSCIRun is a scientific programming environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. We review related systems and introduce a taxonomy that explores different computational steering solutions, Considering these approaches, we discuss why a tightly integrated problem solving environment, such as SCIRun, simplifies the design and debugging phases of computational science applications and how such an environment aids in the scientific discovery process

Optical tomography using the SCIRun problem solving environment: Preliminary results for three-dimensional geometries and parallel processing

We present a 3D implementation of the UCL imaging package for absorption and scatter reconstruction from time-resolved data (TOAST), embedded in the SCIRun interactive simulation and visualization package developed at the University of Utah. SCIRun is a scientific programming environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. While the capabilities of SCIRun's interactive approach are not yet fully exploited in the current TOAST implementation, an immediate benefit of the combined TOAST/SCIRun package is the availability of optimized parallel finite element forward solvers, and the use of SCIRun's existing 3D visualisation tools. A reconstruction of a segmented 3D head model is used as an example for demonstrating the capability of TOAST/SCIRun of simulating anatomically shaped meshes

Grid-enabling problem solving environments: a case study of SCIRun and NetSolve

Journal ArticleCombining the functionality of NetSolve, a grid-based middleware solution, with SCIRun, a graphically-based problem solving environment (PSE), yields a platform for creating and executing grid-enabled applications. Using this integrated system, hardware and/or software resources not previously accessible to a user become available completely behind the scenes. Neither the SCIRun system nor the SCIRun user need to know any details about how these resources are located and utilized. A SCIRun module merely makes an RPC-style call to NetSolve via the NetSolve C language API to invoke a certain routine and to pass its data. Distributed computation and the details of remote communication are completely abstracted away from the SCIRun framework and its end user

Automatic visualization and control of arbitrary numerical simulations

Authors’ preprint version as submitted to ECCOMAS Congress 2016, Minisymposium 505 - Interactive Simulations in Computational Engineering. Abstract: Visualization of numerical simulation data has become a cornerstone for many industries and research areas today. There exists a large amount of software support, which is usually tied to specific problem domains or simulation platforms. However, numerical simulations have commonalities in the building blocks of their descriptions (e. g., dimensionality, range constraints, sample frequency). Instead of encoding these descriptions and their meaning into software architecures we propose to base their interpretation and evaluation on a data-centric model. This approach draws much inspiration from work of the IEEE Simulation Interoperability Standards Group as currently applied in distributed (military) training and simulation scenarios and seeks to extend those ideas. By using an extensible self-describing protocol format, simulation users as well as simulation-code providers would be able to express the meaning of their data even if no access to the underlying source code was available or if new and unforseen use cases emerge. A protocol definition will allow simulation-domain experts to describe constraints that can be used for automatically creating appropriate visualizations of simulation data and control interfaces. Potentially, this will enable leveraging innovations on both the simulation and visualization side of the problem continuum. We envision the design and development of algorithms and software tools for the automatic visualization of complex data from numerical simulations executed on a wide variety of platforms (e. g., remote HPC systems, local many-core or GPU-based systems). We also envisage using this automatically gathered information to control (or steer) the simulation while it is running, as well as providing the ability for fine-tuning representational aspects of the visualizations produced

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

Uintah: a massively parallel problem solving environment

Journal ArticleThis paper describes Uintah, a component-based visual problem solving environment (PSE) that is designed to specifically address the unique problems of massively parallel computation on terascale computing platforms. Uintah supports the entire life cycle of scientific applications by allowing scientific programmers to quickly and easily develop new techniques, debug new implementations, and apply known algorithms to solve novel problems. Uintah is built on three principles: 1) As much as possible, the complexities of parallel execution should be handled for the scientist, 2) software should be reusable at the component level, and 3) scientists should be able to dynamically steer and visualize their simulation results as the simulation executes. To provide this functionality, Uintah builds upon the best features of the SCIRun PSE and the DOE Common Component Architecture (CCA)