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Parallel and Distributed Computing Overview and Syllabus

This course provides a comprehensive exploration of parallel and distributed computing concepts, led by Professor Johnnie Baker with guest lecturer Robert Walker. It covers fundamental principles, architectural features, and programming languages such as MPI and OpenMP. Designed for graduate students, the course includes hands-on assignments, homework, a midterm, and a final exam. Key topics include asynchronous and synchronous computations, multi-processor architectures, and algorithm design techniques, all fundamental for tackling computationally intensive problems.

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Parallel and Distributed Computing Overview and Syllabus

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  1. Parallel and Distributed Computing Overview and Syllabus Professor Johnnie Baker Guest Lecturer: Robert Walker

  2. Instructors • Professor Johnnie W. Baker • Primary Lecturer • Professor Robert Walker • Guest lectures on specific architectures • Guest lecture on his groups VLSI work on parallel architectures • Possible guest lecturers from parallel processing group • Lecture in areas of expertise • Occasionally cover classes when I am away

  3. Prerequisites • Designed to be accessible to all graduate students in computer science • Students who are not CS graduate students may also be qualified to take course

  4. Textbook and References • Textbook • Parallel Programming in C with MPI and OpenMP • Michael Quinn, author • Published by McGraw Hill in 2004 • References for Supplementary Reading • Classroom Slides will also include additional information from a wide range of sources • Any additional needed reference material will be handed out or posted on course website

  5. Some Features of Course • Includes coverage of fundamental concepts of parallel computation, rather than focusing only on latest trends. • Often quickly outdated due to rapid technological changes • Also covers the currently popular cluster architectures, shared memory processors, and the MP language. • This is the focus of the Quinn Textbook

  6. Course Features (cont.) • Covers major types of parallel computation by looking at three key features of each: • Typical architectural features • Typical programming languages used • Typical algorithm design techniques used.

  7. Some Specific Topics • Fundamental concepts applicable to all parallel computation. • Asynchronous (MIMD) distributed memory computation • Message passing communications • Programming using the MPI Language • Architectural features • Examples of typical algorithms

  8. Specific Topics (cont.) • Asynchronous (MIMD) shared memory computation • Symmetric Multiprocessors or SMPs • OpenMP language overview • Synchronous Computation • SIMD, vector, pipeline computing • Associative and Multi-Associative Computing • Programming using the ASC language • MultiC language overview • Fortran 90 and HPF Language overviews • Algorithm examples

  9. Specific Topics (cont.) • Interconnection Networks • Specific Computer Examples including 2D mesh, hypercube, etc. • Synchronous and asynchronous considerations • MIMD-SIMD Comparisons in Real-Time Applications

  10. Some Benefits of Course • While principal focus is on parallel computation, most information is applicable to distributed computing. • There is a wide choice of thesis and dissertation topics in this area • Several professors in department work in this area or make major use of parallel computation in their research • Students working on a thesis or dissertation in another area may benefit from being able to use parallel computation in this work.

  11. Benefits (cont.) • Most large computational problems require a parallel or distributed system to satisfy the speed and memory requirements • Parallel computation currently has major advantages over both distributed computation and grid computation for computational intensive problems. • Programs are normally much simpler • Architectures are much cheaper • Grid computing is currently fairly futuristic

  12. Two Complementary Courses • Parallel & Distributed Computing (Fall) • Architectures • Languages • Parallel Programming • Algorithm Examples for some architectures • Parallel & Distributed Algorithms (Spring) • Important Parallel Models • Designing Efficient Algorithms for Various Models • Will be offered in Spring 2007 • PDC and PDA can be taken in either order • Preference is for PDC to be taken first

  13. Assignments and Grading • Homework assignments • Problems assigned for most chapters • Probably 5-7 different assignments • Some assignments will involve programming • Course Grade • Based on homework, midterm, and final • Approximate weights • Homework 40% • Midterm Exam 30% • Final Exam 30%

  14. Documented Disabilities • If you have documented disabilities, please contact me and make me aware of your needs. • For information on disability accommodations, support, and verification procedure, please see www.kent.edu/sds

  15. Course Website • Will be established quickly • Class slides, assignments, and some references will be posted on this website. • Also, an online reference textbook and a pointer to a second online textbook will be available at this site. • First Assignment – Read Chapter 1 in textbook.

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