![]() Vector field visualization is more difficult to do than scalar field visualization because it generally requires more computing for conveying the directional information and more storage space to store the vector field. ![]() One distinct effort is the development of scalable parallel visualization methods for understanding vector field data. The Ultravis Institute has brought together leading experts from visualization, high-performance computing, and science application areas to make parallel visualization technology a commodity for SciDAC scientists and the broader community. Parallel visualization can be a useful path to understanding data at the ultra scale, but is not without its own challenges, especially across our diverse scientific user community. This article reveals three such enabling technologies that are critical to the future success of scientific supercomputing and discovery. Department of Energy’s SciDAC program, 3 aims to close this gap by developing advanced visualization technologies that enable knowledge discovery at the peta and exa-scale. The Institute for Ultrascale Visualization, 1 2 funded by the U.S. The size and complexity of the data produced by such ultra-scale simulations, however, present tremendous challenges to the subsequent data visualization and analysis tasks, creating a growing gap between scientists’ ability to simulate complex physics at high resolution and their ability to extract knowledge from the resulting massive data sets. ![]() With the current growth rates of supercomputing speed and capacity, scientists are anticipated to study many problems of unprecedented complexity and fidelity and attempt to study many new problems for the first time. Supercomputers give scientists the power to model highly complex and detailed physical phenomena and chemical processes, leading to many advances in science and engineering.
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