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CS 179 The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields. This course will cover programming techniques for the GPU. The course will introduce NVIDIA's parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing. Problem sets will cover performance optimization and specific GPU applications in numerical mathematics, medical imaging, finance, and other fields. Labwork will require significant programming. A working knowledge of the C programming language will be necessary. Although CS 24 is not a prerequisite, it (or equivalent systems programming experience) is recommended. 9 units; third term. |
Instructors/TA's: |
Parker Won - jwon@caltech.edu Nailen Matschke - nailen@caltech.edu Jordan Bonilla - jordan@caltech.edu
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Supervising professors: | Professor
Al Barr - barradmin@cs.caltech.edu |
Time and place: | MWF 3:00-3:55 PM Annenberg 105 |
Office Hours: |
Jordan Bonilla - Tue. 10 PM ~ 12 PM Parker Won - Mon. 8 PM ~ 10 PM Nailen Matschke - Tue. 6 PM ~ 8 PM 104 Annenberg, instructional laboratory |
Grading policy: | Here is the grading scheme for the class:
Homework extensions may be granted if the TA's see it as appropriate. E grades will not be granted except under extreme circumstances. |
Lectures: |
Week 1 (Introduction), MW Parker, F Nailen Lecture 1 (Mon. 04/03): PPT PDF Lecture 2 (Wed. 04/05): PPT PDF Lecture 3 (Fri. 04/07): PPT PDF Week 2 (Shared Memory), MW Parker, F Nailen Lecture 4 (Mon. 04/10): PPT PDF Lecture 5 (Wed. 04/12): PPT PDF Lecture 6 (Fri. 04/14): PPT PDF Week 3 (Reductions, FFT) MW Parker, F Nailen Lecture 7 (Mon. 04/17): PPT PDF Lecture 8 (Wed. 04/19): PPT PDF Lecture 9 (Fri. 04/21): PPT PDF Week 4 (MRI) MW Jordan, F Nailen Lecture 10 (Mon. 04/24): PPT PDF Lecture 11 (Wed. 04/26): PPT PDF Lecture 12 (Fri. 04/28): PPT PDF Week 5 (SVD, streaming, etc) MW Jordan, F Nailen Lecture 13 (Mon. 05/01): PPT PDF Lecture 14 (Wed. 05/03): PPT PDF Lecture 15 (Fri. 05/05): PPT PDF Week 6 (Multi-GPU, etc.) MW Jordan, F Nailen Lecture 16 (Mon. 05/08): PPT PDF Lecture 17 (Wed. 05/10): PPT PDF Week 7 (Recitation and Projects) MW Nailen, F (in-class office hour) TBD Lecture 18 (Mon. 05/15): PPT PDF Week 8 (Projects) MW no class, F (in-class office hour) TBD Week 9 (Projects) MW no class, F (in-class office hour) TBD Week 10 (Projects) MW no class, F (in-class office hour) TBD |
Assignments: |
Lab 1: assignment text UNIX files Windows files Lab 2: assignment text UNIX files Windows files Lab 3: assignment text UNIX files Windows files Lab 4: assignment text UNIX files Windows files Lab 5: assignment text UNIX files Windows files Lab 6: assignment text UNIX files Windows files |
Project | INFO |
Textbook: |
The CUDA Handbook: A Comprehensive Guide to GPU Programming is
recommended but not required. The book is a little more advanced than
other CUDA books and might not be very approachable at first, but by
the end of the course you should find it to be a valuable reference
for both your project as well as any future GPU programming you
find yourself doing.
Buy through
the author's site for a discount.
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CUDA Installation |
Guide Use this code to retrieve your hardware info after you setup CUDA. |
Resources: |
CUDA C Programming Guide List of NVIDIA GPUs Mapping from GPU name to Compute Capability |
Material from previous year(s): |
2015 2016 |
Less useful, but cool resources: |
NVIDIA's Parallel Forall Blog Videos from last several years of NVIDIA's conference on CUDA How to Write Code the Compiler Can Actually Optimize (2015) Excellent CPU optimization manuals What Every Programmer Should Know About Memory GPU focused systems guide to deep learning |