Accelerated Computing

a.k.a. CSE/CMSE 822: Parallel Computing

Term: Spring 2026
Instructor: Xuhao Chen
Lecture: room EB2400, TR 10:20am-11:40am
TAs: Sophia Sun

Welcome!

This is a graduate-level course focused on performance engineering for parallel computers and accelerators. We aim to convey both:

1. First-principles understanding:
   Why do modern accelerator architectures look the way they do?

2. Hands-on experience:
   How do we write fast code for these platforms in practice?

Most of the assignments in this course focus on programming GPUs, for which we use NVIDIA’s CUDA programming language. Students are expected to complete weekly programming assignments, participate in weekly in-person lab sessions, and present a final project at the end of the semester on a topic of their choice.

Topics:

• Parallel & Distributed Programming

• SIMD & GPU Programming 🔥🔥🔥

• Parallel Algorithms and Theory

• Performance Modeling and Optimization

• Machine Learning Systems 🔥🔥🔥

Site Map

• Calendar – Lecture, lab, and office hour schedule; assignment due dates.

• Labs – Descriptions and instructions for all lab assignments.

• Syllabus – Course policies, topics covered, general course information.

• Resources – Links to notes and external materials relevant to the course.

• Contact – How to get in touch with course staff.

• Piazza – Main hub for student discussion and staff communication.

Acknowledgements

This course was originally designed and developed by Prof. Jonathan Ragan-Kelley at MIT. We extend our gratitude to Jonathan for generously sharing the course materials.