All lecture videos are freely available on iTunes U.
GL: Guest Lecture
| week | monday | wednesday | friday | assignments |
|---|---|---|---|---|
| Week 1 (9/2) |
No class Still summer vacation! |
What is Massively Parallel Computing? | Problem Set #0 (Introductions) |
|
| Week 2 (9/7-11) |
No class Labor Day |
Introduction to GPU Computing | ||
| Week 3 (9/14-18) |
Introduction to CUDA | CUDA Lab | CUDA Lab | Problem Set #1 (Getting started with CUDA) |
| Week 4 (9/21-25) |
Advanced CUDA GL: Richard Edgar, IIC / SEAS Using CUBLAS for Quantum Chemistry Leslie Vogt |
CUDA Lab | CUDA Lab | Problem Set #2 (Optimizing CUDA) |
| Week 5 (9/28-10/2) |
Introduction to MPI GL: Richard Edgar, IIC / SEAS |
Parallel Programming Models GL: Greg Morrisett, SEAS & Sukyoung Ryu, Sun |
Problem Set #3 (Image Processing w/ CUDA) |
|
| Week 6 (10/5-9) |
CUDA Lab | CUDA Lab | Intro to EC2 GL: David Malan, SEAS |
Problem Set #4 (GPU Cluster Computing) |
| Week 7 (10/12-16) |
No class Columbus Day |
Introduction to Hadoop GL: Zak Stone, SEAS |
||
| Week 8 (10/19-23) |
Hadoop GL: Philip Zeyliger, Cloudera |
Hadoop Lab | Problem Set #5 (Hadoop & EC 2) |
|
| Week 9 (10/26-30) |
The Larrabee Architecture GL: Larry Seiler, Intel |
PyCuda and GPU Meta Programming GL: Nicolas Pinto, MIT |
||
| Week 10 (11/2-6) |
Parallel Computing in Low-Latency Environments GL: Jim Waldo, Sun |
Modeling Human Vision on the GPU GL: David Cox, Rowland Institute |
Project Proposals due | |
| Week 11 (11/9-13) |
Project Proposal Review | No class Veterans Day |
Project Approvals | |
| Week 12 (11/16-20) |
End of Term Exam | Project Lab | ||
| Week 13 (11/23-27) |
Project Lab | No class Happy Thanksgiving! |
||
| Week 14 (11/30-12/4) |
No class Reading Period |
No class Reading Period |
Final Projects due | |
| Week 15 (12/7-11) |
Final Project Presentations | Final Project Presentations | Project Web Sites due | |
| Week 16 (12/14-18) |
EECS Seminar 2:30-3:30 pm, MD G125 Speaker : Greg Malewicz, Google |
No class | Final grades |
What are the lab sessions?
The lectures focus on concepts and theory, but there's often quite a gap between that and actually getting your code to run. There are a lot of details that are best practiced in a hands-on/tutorial environment with peers. Lab sessions will be held in the instructional computing lab in Room 104 at 53 Church St. where we have 30 MacPro PCs with NVIDIA GPUs. The sessions will be run by the TFs. The lab sessions loosely structured: We will discuss algorithms, share tips and tricks, answer any questions you may have, and address issues that come up. Do not treat this as a license to slack off. You are still expected to come to class and work hard.
What about guest lectures?
As you can see, various people with great expertise in different areas of massively parallel computing have agreed to give guest lectures. I am very excited about this, and would like to sincerely thank them for their time and efforts. Your attendance at guest lectures is mandatory and will count towards your participation grade.
Distance education students are of course exempt from attending the lab sessions and guest lectures in person. However, you can still benefit by joining the live or archived video streams and asking questions through the live skype chat (see Requirements for details).