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CS267 Spring 2025
CS267 Spring 2025
Home
Quizzes
Pre-proposal
Projects
HW 1
HW 2-1
HW 2-2
HW 2-3
HW 3
HW 4
Recordings
UC Berkeley CS267 Home Page
Applications of Parallel Computers
Spring 2025
Tu/Th 11:10am-12:30pm, Soda Hall 306
Please fill the course survey as soon as possible
Instructors:
Teaching Assistants:
Syllabus and Motivation
https://sites.google.com/lbl.gov/cs267-spr2025
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Please
fill the
course
survey as soon as
possible
Home
Quizzes
CS267
Spring
2025
CS267 Spring 2025
Pre-proposal
Projects
HW 1
HW 2-1
HW 2-2
HW 2-3
HW 3
HW 4
Recordings
Instructors:
Aydin Buluc (send email), Office Hours Tuesday 3:30-4:30pm at https://lbnl.zoom.us/j/4442666078.
Jim Demmel (send email), Office Hours Friday 9-10 (in Soda 564 and online at https://berkeley.zoom.us/j/8478008973) and 11-12 (just in Soda 564).
Teaching Assistants:
Rahul Shah (rsha256@berkeley.edu)
Office Hours - Tuesday 1-2pm (zoom), Wednesday 6-7pm (zoom)
Chuao Dong (chuaodong@berkeley.edu)
Office Hours - Friday 1-3pm (zoom)
Yen-Hsiang Chang (yenhsiangc@berkeley.edu)
Office Hours - Monday 1-2pm in Soda 567 (ring the doorbell to get into the SLICE Lab)
Gabriel Raulet (gabe.h.raulet@berkeley.edu)
Office Hours - Thursday 1-2pm hybrid on zoom and in Soda 567
Vinamra Benara (vbenara@berkeley.edu)
Office Hours - Monday 12-1pm (zoom)
To contact the teaching staff, send email to cs267-instructors@lists.eecs.berkeley.edu. This email is monitored by all of us and will therefore lead to a faster
response than emailing one of us individually.
Edstem: Please join our Ed discussion group if you aren't added automatically from bcourses already. We will post assignments and announcements there.
Lectures: 11:10am-12:30pm in 306 Soda Hall. Lectures will be recorded and posted on youtube here: https://www.youtube.com/playlist?list=PLnocShPlKFtKEyUGGDVZNC328a80xbH9 (You need to login with your @berkeley.edu gsuite). The lab sessions will be posted separately
The lectures and labs will also be livecast at the above link. 45
Grading (4-unit version):
Survey: 1%
HW 1: 9%
HW 2.1, HW 2.2, HW 2.3: 9% each
HW 3: 9%
Quizzes: 9%
Project (only for 4-unit course): 45% (pre-proposal, proposal and poster session included)
The grading rubric for the 3-unit version of CS267 will be as follows:
https://sites.google.com/lbl.gov/cs267-spr2025
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Survey 1%
Home Quizzes Pre-proposal
CS267 Spring 2025
HWs: 14% each (there will be 6 in total: 5 existing homeworks + HW4)
CS267 Spring 2025
Projects
HW 1
HW 2-1
HW 2-2
HW 2-3
HW 3
HW 4
Recordings
Quizzes: 15% total
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:
1. Homeworks
2. Pre-proposal
3. Proposal
This policy does NOT apply to the following assignments, for which late submissions will NOT be considered:
1. Quizzes
2. Pre-course survey
3. 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.
https://sites.google.com/lbl.gov/cs267-spr2025
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CS267 Master Schedule Sp25 : Syllabus
CS267 Spring 2025
CS267 Spring 2025
Home
Quizzes
Pre-proposal
Name
Tue, Jan 21 Lecture 1: Introduction & Overview
Thu, Jan 23 Lecture 2: Memory Hierarchies and Matrix Multiplication
Tue, Jan 28 Lecture 3: More MatMul and the Roofline Performance Model
Recitation 1 for C/C++ primer (Jan 28 1-2pm at Soda 310)
Recitation 2 for HW1 (Jan 29 6-7pm at Soda 310)
Projects
HW 1
HW 2-1
HW 2-2
HW 2-3
HW 3
HW 4
Video
video
video
video
video
video
Lecturer
Aydin Buluc
Jim Demmel
Jim Demmel
Rahul
Rahul/Chuao
Thu, Jan 30 Lecture 4: Shared Memory Parallelism
Tue, Feb 4 Lecture 5: Sources of Parallelism and Locality (Part 1)
Thu, Feb 6 Lecture 6a: Sources of Parallelism and Locality (Part 2)
pptx
pptx
pptx
pdf
pdf
pdf
video
video
Aydin Buluc
Jim Demmel
Lecture 6b: Communication-avoiding matrix multiplication (and beyond)
Tue, Feb 11 Lecture 7: Distributed Memory Machines and Programming
Recitation 3 for HW2.1 (Feb 11 1-2pm at Soda 310)
Thu, Feb 13 Lecture 8: Advanced MPI and Collective Communication Algorithms
Tue, Feb 18 Lecture 9: An Introduction to CUDA and Graphics Processors (GPUs)
Thu, Feb 20 Lecture 10: Data Parallel Algorithms (aka, tricks with trees)
pptx pdf
pptx pdf
gslides
pptx pdf
pptx pdf
pptx pdf
video
video
video
video
video
video
Tue, Feb 25 Lecture 11: UPC++: Partitioned Global Address Space Languages
Recitation 4 for HW2.2 (Feb 25 1-2pm at Soda 310)
Thu, Feb 27 Lecture 12: Machine Learning Part 1 (Supervised Learning)
Tue, Mar 4 Lecture 13: Ray: A universal framework for distributed computing
Lecture 14: Machine Learning Part 2 (Unsupervised and semi supervised
CS267 Master Schedule Sp25
pptx pdf
gslides
pptx pdf
pptx pdf
video
video
video
video
Jim Demmel
Aydin Buluc
HW1 Due on Feb 11, HW2 Team Signup For
Yen-Hsiang/Chuao
Aydin Buluc
Aydin Buluc
HW2.2 Released on Feb 18
Jim Demmel
HW2.1 Due on Feb 21
Proposal Page Released on
Kathy Yelick
Feb 26
Chuao/Gabe
Aydin Buluc
Ion Stoica
HW2.3 Released on Mar 4
Syllabus
https://sites.google.com/lbl.gov/cs267-spr2025
Assignment
Survey Due on Jan 23 1pm
HW1 Released on Jan 23
Recordings
PPTX PDF
pptx pdf
pptx pdf
pptx pdf
gslides
gslides
Pre-proposal Due on Jan 31
(4-unit students)
HW2.1 Released on Feb 4
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