SAGE (the Scalable Adaptive Graphics Environment) and its successor SAGE2 (the Scalable Amplified Group Environment) are operating systems for managing content across wideband display environments. This paper documents the prevalent usage patterns of SAGE-enabled display walls in support of the e-Science enterprise, based on nearly 15 years of observations of the SAGE community. These patterns will help guide e-Science users and cyberinfrastructure developers on how best to leverage large tiled display walls, and the types of software services that could be provided in the future.

In today's Big Data era, data scientists require new computational instruments in order to quickly analyze large-scale datasets using complex codes and quicken the rate of scientific progress. While Federally-funded computer resources, from supercomputers to clouds, are beneficial, they are often limiting - particularly for deep learning and visualization - as they have few Graphics Processing Units (GPUs). GPUs are at the center of modern high-performance computing and artificial intelligence, efficiently performing mathematical operations that can be massively parallelized, speeding up codes used for deep learning, visualization and image processing, more so than general-purpose microprocessors, or Central Processing Units (CPUs). The University of Illinois at Chicago is acquiring a much-in-demand GPU-based instrument, COMPaaS DLV - COMposable Platform as a Service Instrument for Deep Learning & Visualization, based on composable infrastructure, an advanced architecture that disaggregates the underlying compute, storage, and network resources for scaling needs, but operates as a single cohesive infrastructure for management and workload purposes. We are experimenting with a small system and learning a great deal about composability, and we believe COMPaaS DLV users will benefit from the varied workflow that composable infrastructure allows.

This paper presents an overview of the tele-immersion applications that have been built by collaborators around the world using the CAVERNsoft toolkit, and the lessons learned from building these applications. In particular the lessons learned are presented as a set of rules-of-thumb for developing tele-immersive applications in general.

The Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago (UIC) has partnered with dozens of computational scientists and engineers to create visualization and virtual reality (VR) devices and applications for collaborative exploration of scientific and engineering data. Since 1995, our research and development activities have incorporated emerging high bandwidth networks like the vBNS and the Internet2 in an effort now called Tele-Immersion. As a result of our six years' experience in building first and second generation VR devices to support these applications, we consider third generation VR devices that will provide desktop/office sized displays. Since no current technology is yet configurable with ideal resolution and size, we will first simulate these devices with available parts, and then build more advanced prototypes. We believe that the devices we propose to build using the new display technologies form a set of desirable human/computer interface requirements for successful Tele-Immersion adoption. A goal of this research is to develop clearly compelling prototypes so that these devices can be improved and reproduced by the private sector.

Article Free Access Share on Computer graphics in higher education (Panel Session) Authors: Al Bork The George Washington University The George Washington UniversityView Profile , Maxine Brown The George Washington University The George Washington UniversityView Profile , Robin King The George Washington University The George Washington UniversityView Profile , Andries van Dam The George Washington University The George Washington UniversityView Profile , Mike Wozny The George Washington University The George Washington UniversityView Profile , Chairman: James D. Foley The George Washington University The George Washington UniversityView Profile Authors Info & Claims SIGGRAPH '83: Proceedings of the 10th annual conference on Computer graphics and interactive techniquesJuly 1983 Pages 31–33https://doi.org/10.1145/800059.801129Online:01 July 1983Publication History 2citation402DownloadsMetricsTotal Citations2Total Downloads402Last 12 Months1Last 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

There is a need for a simple mechanized way to design, update and produce final copies of scores of Labanotation, a symbolic, graphical language for the recording of body movement used by choreographers, anthropologists, psychologists and therapists. This paper discusses the design and implementation of a machine-independent data structure for Labanotation and its application in an interactive storage, retrieval and editing system.

Abstract This paper describes a processor which automatically produces file conversion programs based on non‐procedural user specification. The processor accepts, as input, descriptions of a source file and a desired target file with some auxiliary descriptions of associations between the two. This is specified by a user in a Data Description Language (DDL). To specify validation criteria, complex conversions not built in to the system, security criteria or summary processes, the system also accepts specifications in a Data Manipulation Language (DML). It produces, as an output, a conversion program in PL/1 capable of converting the described source file into the desired target file. The paper describes the structure, system design, capabilities and applications of the DDL/DML language and processor, including an illustrative example.