CS 621 - Parallel and Distributed Computing

Bulletin Description

This course provides graduate students in computer science and in other fields of science and engineering with experience of parallel and distributed computing. It gives an overview of parallel and distributed computers, and parallel computation. The course addresses architectures, languages, environments, communications, and parallel programming. Emphasis on understanding parallel and distributed computers and portable parallel programming with MPI.

Prerequisites

Two 500 level CS courses or consent of the instructor.

Expected Preparation

No previous experience with parallel computers is necessary. Programming skill in a high level programming language such as C or Fortran is required.

Student Learning Outcomes

Students will learn about parallel and distributed computers. They will be able to write portable programs for parallel or distributed architectures using Message-Passing Interface (MPI) library.

Syllabus Information

Possible Textbooks:

Introduction to Parallel Computing (tentative)
by Vipin Kumar, Ananth Grama, Anshul Gupta, and George Karypis

Using MPI: Portable Parallel Programming with the Message-Passing Interface (optional)
by William Gropp, Ewing Lusk, and Anthony Skjellum

 

Course Outline:

This is a sample outline. Exact outline will be determined by the instructor offering this course:

Parallel architectures and communications:

1. limitations of sequential computers

2. SISD, SIMD, MIMD and networked computers

3. shared memory and distributed memory computers

4. static and dynamic interconnections

5. message routing schemes

Performance and scalability

1. speedup, granularity, cost-optimality

2. isoefficiency functions

Amdahl's law and its suitability

Fast Fourier Transform (FFT)

Serial algorithm

Binary-exchange algorithm

Transpose algorithm

Cost-effectiveness of meshes and hypercubes for FFT

Dense matrix computations:

1. striped and checkboard partitionings

2. matrix transposition

3. matrix-vector and matrix-matrix multiplications

Canon's and Fox's algorithms

Gaussian elimination

Message Passing Interface

1. basic MPI functions

2. blocking and nonblocking communications

3. local and global communication functions

4. groups and communicators

5. applications and case studies

Advanced Message Passing in MPI

MPI datatypes

1. matrix algorithms

2. linear systems

Grading:

Exact details about graded work in this course will be determined by the instructor offering the course and will be made available in the syllabus during the first class meeting. Typically, a student's grade will be determined by a weighted average of homework assignments, programming projects, midterm and final examinations. A typical weighting is:

Homework - 20%

Programming Projects - 30%

Midterm Examination - 20%

Final Examination - 30%

Letter Grades: A: 100-86, B: 85-76, C: 75-60, E: 59-0.