Search for: All records

We have developed a series of course-based undergraduate research experiences for students integrated into course curriculum centered around the use of 3D visualization and virtual reality for science visualization. One project involves the creation and use of a volumetric renderer for hyperstack images, paired with a biology project in confocal microscopy. Students have worked to develop and test VR enabled tools for confocal microscopy visualization across headset based and CAVE based VR platforms. Two applications of the tool are presented: a rendering of Drosophila primordial germ cells coupled with automated detection and counting, and a database in development of 3D renderings of pollen grains. Another project involves the development and testing of point cloud renderers. Student work has focused on performance testing and enhancement across a range of 2D and 3D hardware, including native Quest apps. Through the process of developing these tools, students are introduced to scientific visualization concepts, while gaining practical experience with programming, software engineering, graphics, shader programming, and cross-platform design. 

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent. 

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.