CS 610 Semester 2025-26-I

Class hours: MF 8-9 AM Tu 9-10 AM in KD 101

Instructor Information

Name	Email
Swarnendu Biswas	swarnendu@cse.iitk.ac.in

TA Information

Name	Email
Srinjoy Sarkar	srinjoys23@cse.iitk.ac.in
Sahil Basia	sahilbasia24@cse.iitk.ac.in
Nayan Das	nayand24@cse.iitk.ac.in
Sangharsh Nagdevte	sangharsh@cse.iitk.ac.in

Course Description

To obtain good performance, one needs to write correct but scalable parallel programs using programming language abstractions like threads. In addition, the developer needs to be aware of and utilize many architecture-specific features like vectorization to extract the full performance potential. This course will discuss programming language abstractions with architecture-aware development to learn to write scalable parallel programs. This is not a "programming tips and tricks" course.

We will have 4-6 assignments to use the concepts learned in class and appreciate the challenges in extracting performance.

Prerequisites

Exposure to the following courses (or equivalent) is desirable: CS220 (Computer Organization), and CS330 (Operating Systems).
Programming maturity with popular programming languages like C, C++, and Java.

Syllabus

The course will focus on a subset of the following topics.

Challenges in parallel programming, correctness, and performance errors, understanding performance, and performance models
Exploiting spatial and temporal locality with caches, analytical cache miss analysis
Performance bottleneck analysis: PAPI counters, performance analysis tools
Dependence Testing, Loop Transformations
Shared-memory programming and Pthreads
Compiler vectorization: vector ISA, auto-vectorizing compiler, vector intrinsics
Core OpenMP, Advanced OpenMP, Heterogeneous programming with OpenMP
Parallel Programming Models and Patterns
GPU Architecture and CUDA Programming
Shared-memory Synchronization
Concurrent Data Structures

We may add new, drop existing, or reorder topics depending on progress and class feedback. The course may also involve reading and critiquing related research papers.

Policies

Please be on time for class.
Avoid using laptops and/or mobile devices in class, they are distracting for both the instructor and other students who want to concentrate.
You are allowed up to two late days to submit your assignment, with a 25% penalty for each day.

Evaluation Scheme

The following is a tentative allocation and might change slightly depending on the strength of the class. Grading is relative.

Assignments	40%
Midsem	30%
Endsem	30%

Academic Integrity

You may discuss concepts with classmates, but all submissions must be your own or your team's work when teamwork is permitted.
You should not search online for existing solutions related to the assignments, even as a reference.
Students caught cheating or plagiarizing will receive penalties, automatically fail the course, and will be reported to the institute.

Feedback

I am open to feedback about the course content and presentation. Feel free to provide suggestions for improvements.

Resources

Date	Topic	Resources	Recommended Reading
	First course handout	FCH
01/08, 04/08	Compiler Challenges for Parallel Architectures	Slides	AK 1.1-1.6
05/08, 08/08, 09/08	Cache Memory	Slides	HP APP B.1-B.4, 2.1--2.3 CSAPP 6.2-6.4
11/08, 12/08	Write Cache-Friendly Code	Slides	CSAPP 6.5-6.6 DRAG 11.1-11.2
12/08	PAPI Library	Slides
18/08, 19/08, 22/08	Cache Coherence and False Sharing	Slides	MCM Chapters 2, 6, 8 (IITK has subscribed to the ebook)
23/08, 25/08, 26/08	Shared-Memory Synchronization	Slides	MP 2.3, 2.4, 2.6, 7.1-7.5, 8.3 SMS 4.1, 4.2, 4.3.1, 6.1
	Dependence Testing	Slides	AK Chap 2, 3 DRAG 11.6
	Loop Transformations		AK 5.2-5.4, 5.7.2, 5.9, 6.2.1, 6.2.2, 6.2.5, 6.3.1-6.3.4 AP 4.1, 4.2, 4.5, 5.1-5.6 HP 4.5 Compiler Transformations for High-Performance Computing
	Vectorization		HP 4.1-4.2 Program Optimization Through Loop Vectorization Topics in Loop Vectorization
	OpenMP		PP Chapter 5 (IITK has subscribed to the ebook) LLNL OpenMP Tutorial Introduction to OpenMP - Tim Mattson (Intel) OpenMP Application Programming Interface v5.2 OpenMP Application Programming Interface Examples v5.2
	GPU Architecture and CUDA Programming		NVIDIA CUDA C Programming Guide NVIDIA CUDA C Best Practices Guide KH Chapters 1-5,13,20 (IITK has subscribed to the ebook) HP 4.4
	Concurrent Data Structures		MP Chapters 3, 9, 10, 13 (IITK has subscribed to the ebook)

References

I have listed (NOT in any particular order) a few popular references.

[CSAPP]	Computer Systems: A Programmer's Perspective, 3^rd edition - R. Bryant and D. O'Hallaron
[DRAG]	Compilers: Principles, Techniques, and Tools - A. Aho, M. Lam, R. Sethi, and J. Ullman
[HP]	Computer Architecture: A Quantitative Approach, 6^th edition - J. Hennessy and D. Patterson
[AK]	Optimizing Compilers for Modern Architectures - R. Allen and K. Kennedy
[SMS]	Shared-Memory Synchronization, 2^nd edition - M. Scott and T. Brown.
[AP]	Automatic Parallelization: An Overview of Fundamental Compiler Techniques - Samuel P. Midkiff
[PP]	An Introduction to Parallel Programming - Peter S. Pacheco
[KH]	Programming Massively Parallel Processors: A Hands-on Approach, 3^rd edition - David B. Kirk and Wen-mei W. Hwu
[MCM]	A Primer on Memory Consistency and Cache Coherence, 2^nd edition - Vijay Nagarajan, Daniel J. Sorin, Mark D. Hill and David A. Wood
[MP]	The Art of Multiprocessor Programming, 1^st edition - Maurice Herlihy and Nir Shavit

We may read and discuss related materials and research papers, which we will announce in class.