Beyond the PRAM model of computation, multi-process environments often must consider such implementation issues as concurrent accessing of shared data and critical sections of code. This course also will discuss solutions to such issues using semaphores, monitors, or message passing.

Selected algorithms will be implemented on the department's network, using the SR parallel programming language. SR follows a C-like syntax, providing a wide range of parallel constructs. With very few changes, an SR program can move from a single processor environment to a network, with the programmer controlling what runs where.

Henry M. Walker

Office: Science 2420
Telephone: extension 4208
E-mail: walker@math.grin.edu
Office hours are posted weekly on the bulletin board outside my office.
Additional hours can be scheduled by appointment.
If you wish, you may reserve a half hour meeting by signing up on the weekly schedule.

Joseph JáJá, An Introduction to Parallel Algorithms,. Addison-Wesley, 1992.

Stephen J. Hartley, The SR Programming Language: Operating Systems Programming,Oxford University Press, 1995.

The class is scheduled to meet 10:00-noon on Tuesdays and 10:00-11:00 am on Thursdays, from January 20 through March 14 and from March 31 through May 9.

While the schedule for this course is expected to evolve, a Tentative Class Schedule is available.

This course will take advantage of the asymmetric nature of its meeting time (Group B: Tuesdays 10-noon and Thursdays 10-11) by experimenting with several class formats.

• Lecture: Since the traditional lecture format provides an efficient mechanism to present much material in a relatively short time, this format (hopefully with many questions and answers) will be used to introduce some major topics. Many Tuesday classes during the beginning of the semester will begin in this format.

• Laboratory Exercises: The latter part of each Tuesday class will be devoted to hands-on laboratory exercises, which will be done in groups of 2 or 3. These labs will emphasize elements of the SR programming language and details of algorithm implementation.

• Student-Led Discussion: An in-depth comprehension of specific algorithms involves at least three elements: the idea motivating the algorithm, the detailed code of the algorithm, and the application of the algorithm to sample data. To promote active learning and in-depth understanding of specific algorithms, the discussion in some class meetings will be student led. In particular, students should come to class with a written statement of the motivating idea for an algorithm and with a worked-out example showing the algorithm applied to specific data. During classes designated for student-led discussion, students' names will be picked at random to present either their statement of the motivating idea or their example. This class format often will be used on Thursdays for the first part of the semester.

• Student Presentations: After the course covers several fundamental approaches and algorithms, students will choose additional applications areas. During the second half of the semester, many classes will be devoted to student presentations of specific algorithms, based upon material from a section of the textbook. For these talks, students will work in groups of 2 (or occasionally 3), and each student will be expected to collaborate on two presentations.

Course Work will involve a combination of the following activities.

1. Exercises: Written exercises will be assigned to clarify and expand upon algorithms described in class or covered in the text. Students will be exempted from the written exercises based on the material from their presentations. Written exercises normally will be due on Thursdays.

2. Discussion Preparation: As noted under ``Student-Led Discussions'' above, some classes will require students to present the idea motivating an algorithm or an example. On such days, a statement of an algorithm's idea and a worked out example will be due at the start of class. (These statements will be counted as part of the written work for the course.)

3. Programs: Several programming problems will be assigned throughout the semester.

4. Laboratory Exercises: will be due one week after they have been started in class. Thus, labs started one Tuesday will be due at the start of class on the following Tuesday.

5. Tests/Exam: Two hour tests are scheduled for Thursday, February 27, and Thursday, April 17. Following the published exam schedule, an exam is scheduled for 2:00 pm on Wednesday, May 14.

6. Student Talks: As noted under Student Presentations above, each student will collaborate in presenting two algorithms to the class.

Late Penalty: Work is due at the start of class on the date specified in the assignment. A penalty of 33 1/3 % per class meeting will be assessed on any work turned in late, even work submitted at the end of a class.} Thus, work turned in 4 days late will be weighted -33 1/3 %; since a negative score reduces a semester total, it is better not to turn the work in at all.
Exception: Deadlines for programming problems and laboratory exercises are automatically extended at least one class day if the HP network is down for an unscheduled period of 3 or more hours during the week preceding the assignment due date. (In such cases, however, deadlines for written assignments are not extended.)

Absolute Deadline: All homework must be turned in by Thursday, May 8 at 5:00 pm.

The work in this course is split between individual and group work. Students are encouraged to work together on laboratory exercises, discussion preparation, and oral presentations (i.e., please collaborate on course work 2, 4, and 6 above). However, since this course seeks to enable students to develop their own parallel algorithms, collaboration is not allowed on written assignments, programs, or tests (i.e., do NOT collaborate on course work 1, 3, and 5 above).

The final grade will be based upon each student's demonstration of his or her understanding of and facility in programming, not on the performance of the class as a whole nor on a strict percentile basis. While some flexibility may be possible in determining a final semester grade, the following scheme approximates the relative weights attached to various activities in this course.

					Exercises/Summaries:	15%	Labs:	30%	Hour Tests:	25%
					Oral Presentations:	10%	Exam:	20%

This document is available on the World Wide Web as

http://www.math.grin.edu/~walker/courses/295/