UAS & Autonomy Congressional Staffer Briefing
Abstract not provided.
Publications
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Jump to search filtersBeyond Demonstration
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Department of Energy (DOE) Science of Safety Initiative
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U.S. Nuclear Robotics: Yesterday Today Tomorrow: Perspectives on Tomorrow
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Science of Safety Enabling a Next Generation Workforce
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Broadening the Autonomy Discussion
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Unmanned Aerial Systems (UAS) at Sandia
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Unmanned Aerial Systems
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Unmanned Aerial Vehicles A Disruptive Technology
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AI & Automomy Panel
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Briefing for UK Embassy
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RoboRave Presentation
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Unmanned systems and robotics : the next technology revolution
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Climate Changes in the Arctic and The Challenge to USCG Operations
Abstract not provided.
Naval Ship Research Program Sandia capability viewgraphs
Desktop delivery for massive data sets
Abstract not provided.
PDS/PIO: Lightweight libraries for collective parallel I/O
PDS/PIO is a lightweight, parallel interface designed to support efficient transfers of massive, grid-based, simulation data among memory, disk, and tape subsystems. The higher-level PDS (Parallel Data Set) interface manages data with tensor and unstructured grid abstractions, while the lower-level PIO (Parallel Input/Output) interface accesses data arrays with arbitrary permutation, and provides communication and collective I/O operations. Higher-level data abstraction for finite element applications is provided by PXI (Parallel Exodus Interface), which supports, in parallel, functionality of Exodus II, a finite element data model developed at Sandia National Laboratories. The entire interface is implemented in C with Fortran-callable PDS and PXI wrappers.
First Generation ASCI Production Visualization Environments
IEEE Computer Graphics and Applications
The delivery of the first one tera-operations/sec computer has significantly impacted production data visualization, affecting data transfer, post processing, and rendering. Terascale computing has motivated a need to consider the entire data visualization system; improving a single algorithm is not sufficient. This paper presents a systems approach to decrease by a factor of four the time required to prepare large data sets for visualization.For daily production use, all stages in the processing pipeline from physics simulation code to pixels on a screen, must be balanced to yield good overall performance. Also, to complete the data path from screen to the analyst's eye, user display systems for individuals and teams are examined. Performance of the initial visualization system is compared with recent improvements. Lessons learned from the coordinated deployment of improved algorithms are also discussed, including the need for 64 bit addressing and a fully parallel data visualization pipeline.
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