CSci6702 Parallel Computing

Winter 2012

Hours:
Office: Tues 3:00pm or by appt. rm 4242 Mona Campbell,
Class: Tues/Thurs. 1:00pm - 2:30pm, rm 2107 Mona Campbell  

Course Materials:

• Course Overview
• Course Description
• Course Outline
• Class Project
  • Description
  • Topics
  • Project Groups
  • Templates
• Assignments
  • Assignment 1 Readings, Questions
  • Assignment 2

Lectures:

• Introduction to Parallelism
• Parallel Programming
• Parallel Architectures
• Parallel Algorithms
• Parallel Applications
• Other Parallel Models

• Feb 16: Project Groups posted.
• Feb 14: Project topics posted.
• Jan 29: I have posted Assignment 2 which will be due at the start of class on Tuesday Feb 28th.
• Jan 29: I have posted a list of Journals and Conferences in Parallel Computing.
• Jan 3: Assignment 1 readings are here. Assignment 1 questions are here. If you need to brush up your background in analysis of algorithms please see the first 25 slides here. If you like video introductions you might like to watch Lecture 1 and 2 from the Intro Analysis of Algorithms course at MIT.  Erik Demaine the guy teaching lecture 2 did his undergraduate degree at Dalhousie.
• Jan 3: Sign up for a ACEnet account here. You will need my Project Account Number which I will give you during the first class.
• Jan 3: To learn MPI I suggest that you complete the first six sections of the Online Introduction to MPI Course at NCSA. You will need to create a new Account to get started. In order to experiment with MPI while you are learning you will need to setup an MPI environment here at Dalhousie. Our MPI Tutorial will help to get you started.
• Official Outline: This course explores various aspects of parallel computing including parallel architectures, algorithms, systems, programming languages and implementation issues. The focus is on solving real problems on existing parallel machines. Student will be expected to read recent research publications, and participate in significant parallel implementation projects.
• Unofficial Outline: Clusters, stacks of commodity boxes, have taken over the world of parallel computing. Learn how to take a stack of PCs and a Linux  CD and build your own parallel machine. Now for the harder trick - learn how to design algorithms and systems that can take advantage of all those cycles. Be ready to read algorithms papers, write tricky C and MPI code and learn to love your parallel debugger and profiler.