Presented by High-Performance Visualization of Geographic Data Bhaduri L. Budhendra and Alexandre Sorokine Geographic Information Science & Tech Computational.

Slides:

Advertisements

Similar presentations

Components of GIS.

Advertisements

Washington DC October 2012 A Vision for an Advanced Supervision and Control System for the Electric Grid Ramón A. León XM S.A. E.S.P Colombia.

SAN DIEGO SUPERCOMPUTER CENTER Choonhan Youn Viswanath Nandigam, Nancy Wilkins-Diehr, Chaitan Baru San Diego Supercomputer Center, University of California,

1 Cyberinfrastructure Framework for 21st Century Science & Engineering (CIF21) NSF-wide Cyberinfrastructure Vision People, Sustainability, Innovation,

Xingfu Wu Xingfu Wu and Valerie Taylor Department of Computer Science Texas A&M University iGrid 2005, Calit2, UCSD, Sep. 29,

O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY Budhendra Bhaduri Overview of Geospatial Computing at ORNL Geographic Information Science.

Dynamic Modeling of Components on the Electrical Grid Bailey Young Wofford College Dr. Steven Fernandez and Dr. Olufemi Omitaomu Computational Sciences.

IS 466 ADVANCED TOPICS IN INFORMATION SYSTEMS LECTURER : NOUF ALMUJALLY 20 – 11 – 2011 College Of Computer Science and Information, Information Systems.

Focus on Strategic R&D Budhendra Bhaduri, Raju Vatsavai Geographic Information Science and Technology (GIST), Computational Sciences and Engineering Division.

Digital Systems Emphasis for Electrical Engineering Students Digital Systems skills are very valuable for electrical engineers Digital systems are the.

Mobile Online Intelligent Decision Support System Rick Smith, Dr. Stacey Lyle and Dr. Patrick Michaud-Division of Nearshore Research Conrad Blucher Institute.

CPSC 695 Future of GIS Marina L. Gavrilova. The future of GIS.

Office of Science U.S. Department of Energy Grids and Portals at NERSC Presented by Steve Chan.

The Grid and Pervasive Computing Research Cluster at CSE-UNL (aka, the Systems Group) Spanning Research Areas of : Collaborative Technologies, Physical.

Lsevlet: web-based processing tool for LiDAR data J Ramon Arrowsmith December 14, 2004.

ICPCA 2008 Research of architecture for digital campus LBS in Pervasive Computing Environment 1.

Geographic Information System Geog 258: Maps and GIS February 17, 2006.

CS Dept, City Univ.1 Research Issues in Wireless Sensor Networks Prof. Xiaohua Jia Dept. of Computer Science City University of Hong Kong.

F UTURE T RENDS IN GIS. Compared to 10 Years Ago  acquiring data for a new GIS is no longer a major problem.  GPS has become a major sources of new.

Accomplishments. Applications Earth Sciences SIO OptIPuter Visualization Cluster 10 node IBM PC Cluster driving two 9.2 million pixels IBM T221 displays.

Information Technology Infrastructure Needs in Architecture Paperless activities require robust IT solutions Dina Battisto, Ph.D., M.Arch Associate Professor,

Presented by High-Performance Visualization of Geographic Data Budhendra Bhaduri Alexandre Sorokine Geographic Information Science and Technology Computational.

Introduction to GIS. Watershed Discretization (model elements) + Land Cover Soil Rain Results Intersect model elements with Digital Elevation Model (DEM)

Site-Specific Management Factors influencing plant growth Water Light Temperature Soil Compaction Drainage.

Introduction to Computer and Programming CS-101 Lecture 6 By : Lecturer : Omer Salih Dawood Department of Computer Science College of Arts and Science.

ASIACOVER Spatial Database Engine Based on GeoNetwork OpenSource Technology.

September 18-19, 2006 – Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development Using Geographic Information Systems (GIS) as.

1 Distributed Big Data & Analytics University of Cincinnati –- Computational Fluid Dynamics Research - Aerospace Project/Research Title: Study of Active.

Cloud Computing 1. Outline  Introduction  Evolution  Cloud architecture  Map reduce operation  Platform 2.

, Implementing GIS for Expanded Data Accessibility and Discoverability ASDC Introduction The Atmospheric Science Data Center (ASDC) at NASA Langley Research.

27 May 2004 C.N. Papanicolas EGEE and the role of IASA ( In close collaboration with UOA ) IASA GRID Steering Committee: George Kallos Lazaros.

Definition of Computational Science Computational Science for NRM D. Wang Computational science is a rapidly growing multidisciplinary field that uses.

Web mapping interoperability in practice, a Java approach guided by the OpenGis Web Map Server Interface Specification Pedro Fernández, R. Béjar, M.A.

Grid Technologies  Slide text. What is Grid?  The World Wide Web provides seamless access to information that is stored in many millions of different.

VAN HOAI TRAN FACULTY OF COMPUTER SCIENCE & ENGINEERING HCMC UNIVERSITY OF TECHNOLOGY AAOS 2008 Open Grid Computing Architecture.

Academic and pedagogical options in CIM laboratory CIM in universities.

Geographic Information System Dr B P Lakshmikantha Scientist, KSRSAC.

Presented by: La Min Htut Saint Petersburg Marine Technical University Authors: Alexander Bogdanov, Alexander Lazarev, La Min Htut, Myo Tun Tun.

GIS in Egypt Can GIS Industry Meet Its Expected Objectives? Speaker: Mohamed Elshayal Chairman: Dr. Mohamed Hassan 10 February

National Ecological Observatory Network

IPlant Collaborative Hands-on Cyberinfrastructure Workshop – Part 2 R. Walls University of Arizona Biodiversity Information Standards (TDWG) Sep. 29, 2015,

Service - Oriented Middleware for Distributed Data Mining on the Grid ，劉妘鑏 Antonio C., Domenico T., and Paolo T. Journal of Parallel and Distributed.

Issues Autonomic operation (fault tolerance) Minimize interference to applications Hardware support for new operating systems Resource management (global.

CLUSTER COMPUTING TECHNOLOGY BY-1.SACHIN YADAV 2.MADHAV SHINDE SECTION-3.

North American Carbon Program Sub-pixel Analysis of a 1-km Resolution Land-Water Mask Source of Data: The North American sub-pixel water mask product is.

Jonathan Loving Fermi Lab Computing Division

Major Disciplines in Computer Science Ken Nguyen Department of Information Technology Clayton State University.

Computational Sciences & Engineering Division Geographic Information Science and Technology Landsat LIDAR data Hi-res satellite imagery sensor networks.

Futures Lab: Biology Greenhouse gasses. Carbon-neutral fuels. Cleaning Waste Sites. All of these problems have possible solutions originating in the biology.

GIS Data Structures How do we represent the world in a GIS database?

GVis: Grid-enabled Interactive Visualization State Key Laboratory. of CAD&CG Zhejiang University, Hangzhou

GEON2 and OpenEarth Framework (OEF) Bradley Wallet School of Geology and Geophysics, University of Oklahoma

TeraGrid n US based, funded by the NSF n High speed network, combines clusters into one Super-Cluster! n Doesn’t just combine computation… combines storage,

Academic and pedagogical options in CIM laboratory CIM in universities.

CONTENTS: 1.Abstract. 2.Objective. 3.Block diagram. 4.Methodology. 5.Advantages and Disadvantages. 6.Applications. 7.Conclusion.

Securing the Grid & other Middleware Challenges Ian Foster Mathematics and Computer Science Division Argonne National Laboratory and Department of Computer.

Multi Processing Terminology Cycles – Computers execute instructions in cycles, 3 gigahertz means your computer executes 3 billion “cycles” of instructions.

3-D rendering of jet stream with temperature on Earth’s surface ESIP Air Domain Overview The Air Domain encompasses a variety of topic areas, but its focus.

SDDC-ISUAL ISUAL-SDDC/ 1 Current status of the Science Data and Distribution Center (SDDC) H.T. Su, Alfred Chen, P.W. Kao and J.L. Chern ISUAL SDDC, Physics.

Presented by ORNL SensorNet: Wide-Area Sensor Networks for Protection and Assurance Budhendra Bhaduri Geographic Information Science and Technology Computational.

Cluster computing. 1.What is cluster computing? 2.Need of cluster computing. 3.Architecture 4.Applications of cluster computing 5.Advantages of cluster.

Simulation Production System Science Advisory Committee Meeting UW-Madison March 1 st -2 nd 2007 Juan Carlos Díaz Vélez.

Bleeding edge technology to transform Data into Knowledge HADOOP In pioneer days they used oxen for heavy pulling, and when one ox couldn’t budge a log,

Main Research Areas Signal Processing and Communications

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING CLOUD COMPUTING

Simulation Production System

Grid Computing.

Physics-based simulation for visual computing applications

Next-generation Internet architecture

USN Introduction Computer Engineering Sejin Oh.

Presentation transcript:

Presented by High-Performance Visualization of Geographic Data Bhaduri L. Budhendra and Alexandre Sorokine Geographic Information Science & Tech Computational Sciences & Engineering Division

2 Bhaduri_HPC_GIS_0611 Geographic information systems: A short introduction Uses  Government  Homeland security  Resource management  Environmental management A geographic information system (GIS) is an information system to handle geographic data

3 Bhaduri_HPC_GIS_0611 GIS and high-performance computing: Incentives for convergence  Growing size of geographic databases  LIDAR (light detection and ranging) data  Hi-resolution satellite imagery  Sensor networks  Large national datasets (LandScan, Homeland Security Infrastructure Protection)  Integration of multiple data sources  Internet applications  OpenGIS standards  New technologies and analytical methods  Dynamic data analysis  Data mining and visual exploratory methods  Simulation models

4 Bhaduri_HPC_GIS_0611 High-performance visualization architecture for GIS  EVEREST Visualization Cluster  30 x 8 foot viewing area  11,530  3,072 pixel array (35 MP)  27 digital light projectors  14 rendering nodes + 1 head node  Quadrics Elan3 network  Software  OS SUSE Linux  Xdmx distributed X server  GRASS GIS Rendering Node Display Head Node

5 Bhaduri_HPC_GIS_0611 pd-GRASS: Parallel display for GRASS GIS GRASS GIS  Freely available  No license fees  Works on LINUX pd-GRASS  GRASS module for parallel visualization  Full parallelization  Tested with data sets of up to 40 GB  Full GRASS GIS functionality  Available under general public license from

6 Bhaduri_HPC_GIS_0611 pd-GRASS examples 3-arcsecond cell size About 3  10 9 pixel  Shuttle Radar Topography Mission (SRTM) data set

7 Bhaduri_HPC_GIS_0611 High-resolution 3-dimensional view of LIDAR data 10 8 cells Approximately 4 GB LIDAR data set for the city of Houston

8 Bhaduri_HPC_GIS_0611 EDGAR: Electric grid dynamic real-time visualization system Visualization of the electric grid Real-time data collection Approximately 80,000 nodes and 30,000 lines

9 Bhaduri_HPC_GIS_0611 Contacts Budhendra Bhaduri Geographic Information Science and Tech Computational Sciences and Engineering Division (865) Alexandre Sorokine Geographic Information Science and Tech Computational Sciences and Engineering Division (865) Bhaduri_HPC_GIS_0611