May 19, 2005 Presentation

1. May 19, 2005 Presentation • General overview - Barry Kurtz • Set up of grid – Barry Wilkinson • Distributed MATLAB – Rahman Tashakkori • Course overview – Barry Kurtz • Cryptography course - Shan Suthaharan • Image processing course - Rahman Tashakkori • Future of grid computing - Barry Wilkinson • Intelligent decision making course – Dave Powell • Remaining courses – Barry Kurtz • Future Plans – Barry Kurtz

2. Overview Barry Kurtz

3. A Consortium to Promote Computational Science and High Performance Computing • Lead Institution: Appalachian State • Partnering Schools: Elon University, High Point University, Lenoir-Rhyne College, North Carolina A & T, UNC Charlotte, UNC Greensboro, UNC Pembroke, Western Carolina University • Funding: $650,000 • Duration: July 1, 2004 – June 30, 2006

4. www.cs.appstate.edu/nc-hpc

5. Mission • Our mission is to provide the opportunity for undergraduate students at comprehensive universities to study computational science and high performance computing at a level comparable to students at Research I institutions, to promote faculty research by involving undergraduate students in cutting-edge research projects, and to stimulate economic development by promoting grid computing methodologies throughout North Carolina.

6. Vision • Our vision is that by pooling knowledge, resources, and courses at eight collaborating comprehensive universities, by establishing a shared grid computing network, and by exporting grid technology to local IT companies, we can satisfy our mission statement.

7. Goal • Our goal is to provide students at comprehensive universities the opportunity to take advanced courses in computational science and high performance computing at the undergraduate level and allow these students to participate in cutting-edge team-oriented research projects in support of faculty research programs.

8. Faculty and Institutions * Joins the consortium in year two

9. Hardware Acquisitions • A Computer Cluster • Eight Dell Precision 360 or 370 workstations • High Speed Switch • Firewall • There are currently eight clusters purchased; a ninth cluster at UNC Charlotte will be added soon

10. Original Equipment Projections • Year One • $15,000 per cluster budgeted for six schools • $10,000 for partial cluster at two schools • Total allocation: $110,000 • Year Two • $5,000 to complete cluster at two schools • Total allocation: $10,000 • Actual Equipment Costs • Six months after the projected costs were estimated, the actual equipment (identical to the promised equipment) only cost $12,000 per cluster

11. Actual Equipment Expenditures • Year One • $12,000 per cluster for eight schools (the two partial clusters are made complete) • An additional cluster for Barry Wilkinson who is returning to UNC Charlotte (our ninth partner) • $2,000 is still left in reserve • Year Two • $10,000 to complete the partial clusters is available • Combined with the $2000 in reserve we can fund a tenth cluster for another UNC school that is willing to join our consortium

12. Software • Grid Computing Software • Globus • Condor • MATLAB • Toolboxes: Wavelets, Signal Processing, Image Processing, Neural Networks, Statistics, Bioinformatics • Distributed MatLab • MPI • Standard MPI • Grid-enabled MPI

13. Set up of grid Barry Wilkinson

14. Grid Support • Goals • Create a working grid with clusters at every university in the consortium. • Use that grid in courses that are jointly taught by faculty from several of the universities. Participating Sites

15. Current Status • Summer 2004: Initial installation of the NMI Distribution including Globus Toolkit 3.2 and Condor-G • Summer 2004: Developed projects testing the distribution (web services, grid services, GRAM, Condor-G) • Summer 2004: Distribution CD developed • Summer 2004: First load testing in summer workshop at ASU Participating Sites

16. Current Status • Fall 2004: Clusters acquired and installed at all sites • Fall 2004: All clusters operational with basic functionality: Linux operating system and cluster software Participating Sites

17. Current Status • Fall 2004: Intense testing of the grid software in the Grid Computing course taught by Barry Wilkinson • Fall 2004: Extended the software being used to include a MPI project developed by Barry Wilkinson and a grid workflow editor, GridNexus, developed at UNC-Wilmington Participating Sites

18. Current Status • Spring 2005: Installation of a newer NMI Distribution (still GT3.2) at Elon and WCU • Spring 2005:Testing of the new version of the software in the Intelligent Decision Making course at Elon and WCU • Spring 2005: Joel Hollingsworth developed a new grid services project Participating Sites

19. Future Plans • Summer 2005: Installation of a newer NMI Distribution (changing to GT4!) • Summer 2005: Redo all the projects using GT4; develop more projects • Summer 2005: Retest everything in the summer workshop • Summer 2005: Create a certificate authority at Elon to allow multi-site operation Participating Sites

20. Future Plans • Fall 2005: Test the new installations and project versions in Barry Wilkinson’s Grid Computing course • Fall 2005: Further test the grid software in the capstone course and the other courses • Spring 2006: Revise the projects (and perhaps reinstall) based on the Fall experience • Spring 2006:Use in the spring courses Participating Sites

21. Summary • It has been a learning experience for everyone involved. • It has been a successful experience with students getting working experience with grid software. • Teamwork and inter-university collaboration have been the keys to our success. Participating Sites

22. Summary • Much remains to be done • transition to GT4 • project revisions • additional projects • certificate authority and multi-site grid operation Participating Sites

23. Distributed MATLAB Rahman Tashakkori

24. Distributed Toolbox The MathWorks web site was the main source of the MATLAB related information on this presentation The Distributed Computing Toolbox works with the MATLAB Distributed Computing Engine to execute coarse-grained MATLAB algorithms and Simulink models in a cluster of computers It allows prototyping and developing applications in the MATLAB environment and then use the Distributed Computing Toolbox to divide them into independent tasks. The MATLAB Distributed Computing Engine evaluates these tasks on remote MATLAB sessions.

25. Distributed Toolbox: Key Features • Distributed execution of coarse-grained MATLAB algorithms and Simulink models on remote MATLAB sessions • Control of the distributed computing process via a function-based or an object-based interface • Distributed processing on both homogeneous and heterogeneous platforms • Support for synchronous and asynchronous operations • Access to single or multiple clusters by single or multiple users https://tagteamdbserver.mathworks.com/ttserverroot/Download/23958_91263v01_ML_DCT_DS.pdf

26. Distributed Toolbox http://www.mathworks.com/products/distribtb

27. Creating and Submitting Jobs with the Distributed Computing Toolbox • The toolbox includes functions for defining jobs, dividing them into tasks, sending them to the MATLAB Distributed Computing Engine for execution, and retrieving the results. The complete process includes five steps: • Finding a job manager • Creating a job • Creating tasks • Submitting the job to the job queue • Retrieving results

28. Problems • Network has to be able to do multicast • It is not X-term friendly, best to run the jobs from console • All machines should have the same version of MATLAB • It requires all Unix-based machines (manager and workers) to run the same version of kernel • In case that a firewall is used, specific ports should be left open to establish workers-server and server-license manager connectivity

29. Course Overview Barry Kurtz

30. Courses

31. Research Outcomes • Publications • 2 in print • 2 under review • Presentations • 7 completed • Projects • 13 completed • 2 planned for summer • Follow Up Grant Proposals • 3 planned for the National Science Foundation • 1 planned for another agency

32. Cryptography and Network Security Shan Suthaharan

33. Cryptography and Network Security • Host Institution – UNC Greensboro • Dr. Shan Suthaharan, primary instructor • Fall 2004, 15 students at UNCG • Remote Classrooms • Dr. Barry Kurtz, AppState, team instructor • 2 students at AppState

34. Elliptic CurveCryptography Stephanie Rednour worked under the direction of B. Kurtz on this special research project

35. GUI for RC5 Encryption • Ramu Pulipati worked under the direction of Shan • Suthaharan on this special research project.

36. Digital Image Processing Rahman Tashakkori

37. Digital Image Processing • Host Institution – ASU • Dr. Rahman Tashakkori • Fall 2004, 20 students at ASU • Remote Classrooms • Dr. Sue Lea, UNCG • 2 students at UNCG

38. Major Topics • Introduction • Mathematical Background • Fundamentals • Intensity Transformations and Spatial Filtering • Frequency Domain Processing • Image Restoration • Image Compression • Wavelet Analysis

39. Image Processing Projects, ASU These projects were completed under the direct supervision of Dr. Tashakkori • Investigating content-based search techniques in medical images of different modalities – Steve Heffner This project lead to Steve’s honor’s thesis An Efficient Medical Image Content-based Search Approach, May 2005 • Parallel Implementation of lifting schemes – Tim Racz This project lead to Tim’s M.S. thesis Parallel implementation of Hexagonal Lifting Schemes, In process

40. Image Processing Projects, ASU Other Projects: • A Java-based enhancement Toolbox. This toolbox includes several different modules that are developed by 8 students throughput the semester. • A C++-based enhancement Toolbox. This toolbox includes several different modules that are developed by 5 students. • Histogram equalization implementation of color images • A morphing and edge detection Toolbox. This toolbox includes three different modules that are developed by 3 of our students throughout the semester • An image cropping Toolbox. • A high- and low-pass filtering Toolbox

41. Image Processing Project, UNCG • David Waizenegger and Sarah Parker worked on this project under Dr. Lea’s supervision • The students investigated time sequences of (ocean) wave crest images. The wave crest data is not clean data; crests may be erroneously connected to form contours. Students were asked to come up with an automatic technique for determining the angle through which the image needed to be rotated in order to make the crests approximately parallel to the bottom of the rotated image. Accomplishing such a rotation simplifies analysis of the sequence of images to infer the bottom depth near the shore.

42. Future Plans – Fall 2005 • Continue teaching the course in the consortium • Expand and improve existing Toolboxes • Create new Toolboxes • Establish a team-based research problems to includes collaboration across campuses • Utilize the grid to solve some of the image processing problems

43. Student Presentations • Steve Heffner, Investigating content-based search techniques in medical images of different modalities, The 8th Annual Celebration of Student Research and Creative Endeavors, April 19, Appalachian State University

44. The Future of Grid Computing Barry Wilkinson

45. Intelligent Decision Making Dave Powell

46. Intelligent Decision Making Elon Instructors: Powell, Hollingsworth WCU Instructor: Holliday Using Inexpensive High Performance Computing to design products faster, better and cheaper.

47. Overview • Motivation: Need for High Performance Computing for Global Competitiveness • Course Enrollment, Topics and Projects • Ideal Capstone Course • Key Benefits • Key Pedagogies for NCREN • Key Challenges • Student Survey Suggestions • Research Outcomes • Future Work

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