Modern scientific, engineering and medical computational simulations, as well as experimental and observational data sensing/measuring devices, produce enormous amounts of data. While statistical analysis provides insight into this data, scientific visualization is tactically important for scientific discovery, product design and data analysis. These benefits are impeded, however, when scientific visualization algorithms are implemented from scratch — a time-consuming and redundant process in immersive application development. This process can greatly benefit from leveraging the state-of-the-art open-source Visualization Toolkit (VTK) and its community. Over the past two (almost three) decades, integrating VTK with a virtual reality (VR) environment has only been attempted to varying degrees of success. In this paper, we demonstrate two new approaches to simplify this amalgamation of an immersive interface with visualization rendering from VTK. In addition, we cover several enhancements to VTK that provide near real-time updates and efficient interaction. Finally, we demonstrate the combination of VTK with both Vrui and OpenVR immersive environments in example applications.

A key trend facing extreme-scale computational science is the widening gap between computational and I/O rates, and the challenge that follows is how to best gain insight from simulation data when it is increasingly impractical to save it to persistent storage for subsequent visual exploration and analysis. One approach to this challenge is centered around the idea of in situ processing, where visualization and analysis processing is performed while data is still resident in memory. This paper examines several key design and performance issues related to the idea of in situ processing at extreme scale on modern platforms: scalability, overhead, performance measurement and analysis, comparison and contrast with a traditional post hoc approach, and interfacing with simulation codes. We illustrate these principles in practice with studies, conducted on large-scale HPC platforms, that include a miniapplication and multiple science application codes, one of which demonstrates in situ methods in use at greater than 1M-way concurrency.

Abstract The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i. e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme‐scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.

Advanced modeling and simulation has enabled the design of a variety of innovative products and the analysis of numerous complex phenomenon. However, significant barriers exist to widespread adoption of these tools. In particular, advanced modeling and simulation: (1) is considered complex to use, (2) needs in-house expertise, and (3) requires high capital costs. In this paper, we describe the development of an end-to-end, advanced modeling and simulation cloud platform that encapsulates best practices for scientific computing in the cloud, and demonstrate using Hydra-TH as a prototypical application. As an alternative to traditional advanced modeling and simulation workflows, our Web-based approach simplifies the processes, decreases the need for in-house computational science and engineering experts, and lowers the capital investments. In addition to providing significantly improved, intuitive software, the environment offers reproducible workflows where the full lifecycle of data from input to final analyzed results can be saved, shared, and even published.

Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.

The availability of low-cost virtual reality (VR) systems coupled with a growing population of researchers accustomed to newer interface styles makes this a ripe time to help domain science researchers cross the bridge to utilizing immersive interfaces. The logical next step is for scientists, engineers, doctors, etc. to incorporate immersive visualization into their exploration and analysis workflows. However, from past experience, we know having access to equipment is not sufficient. There are also several software hurdles to overcome. Obstacles must be lowered to provide scientists, engineers, and medical professionals low-risk means of exploring technologies beyond their desktops.

P Vh4 is an inexpensive, but extremely effective tool which allows a researcher to use workstations as nodes in a parallel processing environment to perform largescale computations.The numerical approximation and visualization of seismic waves propagating in the earth strains today's largest supercomputers.We present timings and visualization for large earth models run on a ring of IBM RS/6000's which illustrate P VM'S capability of handling large-scale problems.We will show that PVM can effectively compete with traditional supercomputers.Any researcher with accounts on UNIX-based workstations can corral unused CPU cycles to solve large-scale problems.

Article Free AccessVector/parallel implementation of a porous media flow code Share on Authors: R. Ewing Institute for Scientific Computation, University of Wyoming, Laramie, Wyoming Institute for Scientific Computation, University of Wyoming, Laramie, WyomingView Profile , P. O'Leary Institute for Scientific Computation, University of Wyoming, Laramie, Wyoming Institute for Scientific Computation, University of Wyoming, Laramie, WyomingView Profile , J. Sochacki Institute for Scientific Computation, University of Wyoming, Laramie, Wyoming Institute for Scientific Computation, University of Wyoming, Laramie, WyomingView Profile Authors Info & Claims Supercomputing '91: Proceedings of the 1991 ACM/IEEE conference on SupercomputingAugust 1991 Pages 294–303https://doi.org/10.1145/125826.125997Online:01 August 1991Publication History 0citation317DownloadsMetricsTotal Citations0Total Downloads317Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF