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CS/EE 217 – GPU Architecture and Parallel Programming

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Presentation on theme: "CS/EE 217 – GPU Architecture and Parallel Programming"— Presentation transcript:

1 CS/EE 217 – GPU Architecture and Parallel Programming
Introduction Daniel Wong, Assistant Professor Department of Electrical and Computer Engineering Cooperating Faculty, Department of Computer Science and Engineering

2 Welcome!

3 About me Born and raised in Los Angeles / SGV
University of Southern California BS Computer Engineering/Computer Science ‘09 MS Electrical Engineering ‘11 PhD Electrical Engineering ’15 Research Interest Energy Efficient Computer Systems GPGPUs, Data Centers Industry Experience Samsung Semiconductor, Inc. Lawrence Livermore National Labs Joined UCR in 2015

4 Course Goals Learn how to program GPGPU processors and achieve
high performance functionality and maintainability scalability across future generations Technical subjects principles and patterns of parallel algorithms processor architecture features and constraints programming API, tools and techniques

5 Logistics Course Website Assignments/Projects Discussion/Help
Check often for announcements Assignments/Projects iLearn (iLearn.ucr.edu) Discussion/Help Piazza ( ENGR Account Setup

6 Textbook D. Kirk and W. Hwu, “Programming Massively Parallel Processors – A Hands-on Approach, Second Edition” CUDA by example, Sanders and Kandrot Nvidia CUDA C Programming Guide

7 Attendance/Grading Attendance Grade Breakdown
You are expected to attend all lectures. Some slides only make sense in lecture.  Grade Breakdown Labs: 35% Exam and Final: 35% Project: 30% Class Participation/Extra Credit: 5%

8 Lab Policies 3 slip days 15% penalty per late day
If it’s one minute late, it’s still late No extensions will be given All labs/projects are due at the end of the due date (midnight) Projects should be uploaded to iLearn

9 Contact Instructor: Daniel Wong TA: Marcus Chow
Homepage: Office: WCH 425 Office Hours: by appointment TA: Marcus Chow Office Hours: TBD


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