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In Situ Summarization for Existing Petascale and Future Exascale HPC Infrastructures

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-17-P-0038
Agency Tracking Number: A17A-010-0071
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A17A-T010
Solicitation Number: 2017.0
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-06-30
Award End Date (Contract End Date): 2017-12-31
Small Business Information
28 Corporate Drive
Clifton Park, NY 12065
United States
DUNS: 010926207
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 David Thompson
 Staff Research & Development Engineer
 (518) 371-3971
Business Contact
 Vicki Rafferty
Phone: (518) 371-3971
Research Institution
 North Carolina State University
 Sherrie Settle
2701 Sullivan Drive Suite 240
Raleigh, NC 27696
United States

 (919) 515-2444
 Nonprofit College or University

We propose extending our existing, scalable in situ visualization and analysis infrastructure -- based on VTK, VTK-m, and SENSEI -- to include summarization and compression algorithms. In Phase I, we will investigate summarization and compression algorithms, comparing wavelet-based techniques cited in the call to statistics-on-features, sorting-based compression, and feature-based extracts that provide post-hoc flexibility with low storage requirements. One example of feature-based extracts are renderings of simulation data where each pixel stores -- instead of a color -- (1) a scalar field value, (2) a depth into the scene from the camera's focal point, and (3) the surface normal of geometry at that pixel. These values can be used post-hoc to allow flexible exploration: they can be composed with other renderings of different features from the same viewpoint, relighted to clarify surface shape, and recolored to capture both small-scale and large-scale variations in scalar values. Beyond an investigation into promising new techniques, the Phase I work will also include an evaluation of algorithm scalability using simple surrogate algorithms targeting 2 many-core architectures: Intel MIC and nVidia Tesla.

* Information listed above is at the time of submission. *

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