| Instructor (Lectures) | Sreepathi Pai (sree at cs dot rochester dot edu) |
| Instructor (Labs) | Alex Page (alex.page at rochester dot edu) |
| Class Location (both lectures/labs) | CSB 523 |
| Time | MW 1650--1805 |
| Office Location and Hours | (Pai) 3409 Wegmans, by appointment, but you're welcome to drop by between 10AM and 5PM and see if I'm around |
All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available at: http://www.rochester.edu/college/honesty.
(from CDCS) GPU micro-architecture, including global memory, constant memory, texture memory, SP, SM, scratchpad memory, L1 and L2 cache memory, multi-ported memory, register file, and task scheduler. Parallel programming applications to parallel sorting, reduction, numeric iterations, fundamental graphics operations such as ray tracing. Desktop GPU programming using Nvidia's CUDA (Compute-Unified Device Architecture). CPU/GPU cooperative scheduling of partially serial/partially parallel tasks.
(mine) Parallel programming is necessary to obtain performance on modern computers. Graphics Processing Units (GPUs) are processors that support massive parallelism. In this course, we will learn how to parallelize programs and run them on the GPU. Since the GPU is a fairly primitive processor, getting good performance on the GPU is hard compared to CPUs and requires programmers to be highly knowledgeable of the internals of GPU architecture. This course will cover NVIDIA's CUDA programming language, and all the internals of NVIDIA GPUs required to write fast programs.
ECE 200, or ECE 216, or ECE 201/401, or equivalent. Familiarity with assembly language and C programming language. Instructor approval.
There will be no mid-term or final exams for this course.
There will be 4 programming assignments (65%) of the grade, and 1 project (35%) of the grade.
Students will be expected to present their project to the rest of the class and also submit a project report.
Beyond 1 day will not be graded except at instructor's discretion.
Within 1 day of due date will be penalized 10% of the grade.
There are no required textbooks for this class.
The following resources are useful general references:
Lecture-wise resources are given below in the schedule.
| Date | Topic | Assignments |
| September 4 | Holiday (Labour Day) | |
| September 6 | Introduction | |
| September 11 | Lab (Introduction to pthreads) | |
| September 13 | Synchronization (Guest lecture: Prof. Scott) | |
| September 18 | Lab | |
| September 20 | Parallelizing Programs | |
| September 25 | Lab | |
| September 26 | A1 (rel. Sep 27) | |
| September 27 | Understanding Memory Performance | |
| October 2 | Lab | |
| October 4 | Optimizing for Caches | |
| October |
A1 due 7PM | |
| October 9 | Holiday (Fall Term Break) | |
| October 11 | Introduction to GPUs | Project proposal discussion period starts |
| October 16 | Lab | A2 |
| October 18 | CUDA Programming | |
| October 23 | Lab | |
| October 25 | GPU Architecture I (Execution) | |
| October 27 | A2 due | |
| October 30 | Lab | A3 (released Nov 2) |
| November 1 | GPU Architecture II (Memory) | Project proposal due (extended to November 7) |
| November 6 | Lab | |
| November 7 | Project proposal due | |
| November 8 | Synchronization and Communication | |
| November 12 | ||
| November 13 | Lab | |
| November 15 | Heterogeneous Parallelism | Project report draft #1 due |
| November 20 | Lab | |
| November 21 | ||
| November 22 | Holiday (Thanksgiving Break) | A3 due (extension for BlueHive outage) |
| November 27 | Lab | |
| November 29 | High Level GPU programming | |
| December 1 | Project report draft #2 due, A4 | |
| December 4 | Lab | |
| December 6 | Wrap-up and Project reviews | |
| December 11 | Project Presentations | |
| December 13 | A4 due |
Last updated: 1 Dec 2017