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VisIt is a popular open source tool for visualizing and analyzing data. It owes its success to its foci of increasing data understanding, large data support, and providing a robust and usable product, as well as its underlying design that fits today's supercomputing landscape. In this short paper, we describe the VisIt project and its accomplishments. 1.(More)
Data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small(More)
In recent years, there has been significant growth in the use of patient-specific models to predict the effects of neuromodulation therapies such as deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge, primarily due to the complexity of the models and the(More)
In situ visualization has become a popular method for avoiding the slowest component of many visualization pipelines: reading data from disk. Most previous in situ work has focused on achieving visualization scalability on par with simulation codes, or on the data movement concerns that become prevalent at extreme scales. In this work, we consider in situ(More)
Coupling visualization and analysis software with simulation code is a resource-intensive task. As the usage of simulation-based science grows, we asked ourselves: what would it take to enable in situ visualization for every simulation in existence? This paper presents an alternative view focusing on the approachability of in situ visualization. Utilizing a(More)
Accepted for publication by IEEE. ©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse(More)
Ray tracing in scientific visualization allows for substantial gains in performance and rendering quality with large scale polygonal datasets compared to brute-force rasterization, however implementing new rendering ar-chitectures into existing tools is often costly and time consuming. This paper presents a library, GLuRay, which intercepts OpenGL calls(More)