The Spring 2022 class will start virtually online for the first two weeks and the course videos will be posted here.
Graduate Student Instructors:
Vivek Bharadwaj (send email)
Office Hours M 1-2 PM PST, W 2-3 PM PST at this zoom OR Soda 580 (within the SLICE lab; ring doorbell for entrance)
Zhen Dong (send email)
Office Hours Wednesday 2-3 PM. Appointments can be made here.
Guanhua Wang (send email)
Office Hours Tue 6PM - 7PM PST. Appointments can be made here.
Lianmin Zheng (send email)
Office Hours Monday 6-7 PM. Appointments can be made here.
To contact the teaching staff, send email to email@example.com. This email is monitored by all of us and will therefore lead to a faster response than emailing one of us individually.
Piazza: Please join our Piazza Group. We will post assignments and announcements there.
Lectures: 11am-12:30pm in 145 Dwinelle Hall Hall 145
HW 1: 9%
HW 2.1, HW 2.2, HW 2.3: 9% each
HW 3: 9%
Project: 45% (Pre-proposal and proposal and virtual poster session included)
Late Policy: 2% of assignment worth deducted every day past your due date. NO CREDIT after 10 days. This policy applies to the following assignments:
This policy does NOT apply to the following assignments:
Final project poster and report
Syllabus and Motivation
CS267 was originally designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets. CS267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented. CS267 satisfies part of the course requirements for the Designated Emphasis ("graduate minor") in Computational Science and Engineering.
While this general outline remains, a large change in the computing world started in the mid 2000's: not only are the fastest computers parallel, but nearly all computers are becoming parallel, because the physics of semiconductor manufacturing will no longer let conventional sequential processors get faster year after year, as they have for so long (roughly doubling in speed every 18 months for many years). So all programs that need to run faster will have to become parallel programs. (It is considered very unlikely that compilers will be able to automatically find enough parallelism in most sequential programs to solve this problem.) For background on this trend toward parallelism, click here.
Students in CS267 will get an overview of the parallel architecture space, gain experience using some of the most popular parallel programming tools, and be exposed to a number of open research questions. The lectures will also cover a broad set of parallelization strategies for applications covering numerical simulation and data analysis to machine learning.