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Open Access Optimizing for Visual Cognition in High Performance Scientific Computing

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High performance scientific computing is undergoing radical changes as we move to Exascale (1018 FLOPS) and as a consequence products for visualization must increasingly be generated in-situ as opposed to after a model run. This changes both the nature of the data products and the overall cognitive work flow. Currently, data is saved in the form of model dumps, but these are both extremely large and not ideal for visualization. Instead, we need methods for saving model data in ways that are both compact and optimized for visualization. For example, our results show that animated representations are more perceptually efficient than static views even for steady flows, so we need ways of compressing vector field data for animated visualization. Another example, motion parallax is essential to perceive structures in dark matter simulations, so we need ways of saving large particle systems optimized for perception. Turning to the cognitive work flow, when scientists and engineers allocate their time to high performance computer simulations their effort is distributed between pre and post run work. To better understand the tradeoffs we created an analytics game to model the optimization of high performance computer codes simulating ocean dynamics. Visualization is a key part of this process. The results from two analytics game experiments suggest that simple changes can have a large impact on overall cognitive efficiency. Our first experiment showed that study participants continued to look at images for much longer than optimal. A second experiment revealed a large reduction in cognitive efficiency as working memory demands increased. We conclude with recommendations for systems design.
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Document Type: Research Article

Publication date: 2016-02-14

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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