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Collaborative Scientific Visualization: from your lab to Internet2 and beyond

Collaborative Scientific Visualization: from your lab to Internet2 and beyond. Matthew Wolf College of Computing Georgia Institute of Technology. Work done in collaboration with Dr. Karsten Schwan Dr. Greg Eisenhauer Zhongtang Cai Weiyun Huang Doug Spearot. www.cercs.gatech.edu.

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Collaborative Scientific Visualization: from your lab to Internet2 and beyond

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  1. Collaborative Scientific Visualization: from your lab to Internet2 and beyond Matthew Wolf College of Computing Georgia Institute of Technology • Work done in collaboration with • Dr. Karsten Schwan • Dr. Greg Eisenhauer • Zhongtang Cai • Weiyun Huang • Doug Spearot www.cercs.gatech.edu

  2. “Collaborate or Die!” • Within the academic scientific community, collaboration, both internal and external, has become a key part of the funding formula • Distributed collaboration has become a necessity. • (The above directive is drawn from a slide title at a recent DOE meeting)

  3. vs. Undamaged Damaged Undamaged W W L L W / l and L / l l = interatomic spacing Research Application: Molecular Dynamics • Mechanical Engineering • Physics • Chemistry • Aerospace Engineering Twinning Plane FCC

  4. Data Channels Directory Services Data Video High Bandwidth Data Video Video Distributed PowerPoint Video Audio local storage Personalized Viewpoints:The Scientific Data Portal Data Channels have • Labeling • Synchronization • Replay • User-defined • transformation • High performance • Handle “large” • data feeds • Quality of • service

  5. Sample Scientific Data • Non-typical, but sample nonetheless. • Want (close to) real time visualization, photo-realistic, with ability to annotate & discuss remotely. From Science, 18 August 2000. M. Wolf, M. Moseler, and U.Landman

  6. Event Source Event Sink Event Sink Event Sink Event Source Event Channel Model Event Channel

  7. ECho: Event-based Data Exchange • Represent information flows as event streams (publish/subscribe model) • Transparency of local and remote receivers via event channels, implemented with underlying peer-to-peer communications over TCP, UDP, Wireless, multicast, etc. • Efficient, fully typed binary data transmission, based on dynamically defined event formats (PBIO) • Dynamic extension of existing formats and discovery/operation on format contents (reflection) • Interoperate with CORBA and Java (JECho) via IDL and XML

  8. Source F( ) ECho: Source-based Filtering: Achieving Data Personalization via Dynamic Code Generation Sink F( ) Source Filter Function: { if ((data inside viewing area) and ((data values inside useful ranges)) { return 1; /* submit into channel */ } return 0; /* suppress data */ }

  9. Filter Example This filter computes and returns the average of its input array: {int i;int j;double sum = 0.0;for (i = 0; i < MAXI; i = i + 1) { for (j = 0; j < MAXJ; j = j + 1) { sum = sum + input.array[i][j]; }}output.avg_array = sum / (MAXI * MAXJ);return 1; /* submit record */ } Filters are the basic units of composition. To deal with heterogeneity, portable filters are written in ECL, a subset of a general procedural language (C), and a native version of a given filter’s code can be dynamically generated at the destination. We support dynamic code generation for MIPS, Alpha, Sparcs, and x86 processors.

  10. Overview of the Smart PointerVisualization System Filters

  11. Personalized Viewpoints:The Smart Pointer

  12. The Personal Connection : • The Access Grid • http://www.accessgrid.org • A large-group teleconferencing facility • The human interactions interface to grid computing • Core middleware with support for for multimedia streams, interfaces to grid data, and data visualizations

  13. Data Channels Directory Services Data Video High Bandwidth Data Video Video Distributed PowerPoint Video Audio local storage Personalized Viewpoints:The Scientific Data Portal (revisited) Data Channels have • Labeling • Synchronization • Replay • User-defined • transformation • High performance • Handle “large” • data feeds • Quality of • service

  14. Adding Quality of Service • QoS is important once you begin doing steering as well as visualization. • Working on “late” or “old” data is unacceptable • Need to enhance the network-level QoS capabilities with application-level support • May want to change the data stream (b/w vs color, etc) • Smart Pointer itself is an example

  15. IQ-ECho: Adapting Applications and Platforms Host Application Component Application Component Host Middleware OS OS Quality Attributes Extension Modules Active NI Active NI Extensible Platforms

  16. ECho References • The ECho Event Delivery System, Greg Eisenhauer • A Middleware Toolkit for Client-Initiated Service Specialization, Greg Eisenhauer, Fabian Bustamente and Karsten Schwan, Proceedings of the PODC Middleware Symposium - July 18-20, 2000 • Event Services for High Performance Computing, Greg Eisenhauer, Fabian Bustamente and Karsten Schwan, Proceedings of High Performance Distributed Computing (HPDC-2000) • JECho - Supporting Distributed High Performance Applications with Java Event Channels, Dong Zhou, Karsten Schwan, Greg Eisenhauer and Yuan Chen, Cluster2000 Downloadable versions available from http://www.cc.gatech.edu/systems/projects/ECho/

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