About this Course

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This course will help prepare students for developing code that can process large amounts of data in parallel. It will focus on foundational aspects of concurrent programming, such as CPU/GPU architectures, multithreaded programming in C and Python, and an introduction to CUDA software/hardware.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 4 in the

GPU Programming Specialization

Beginner Level

Some experience programming

Approx. 19 hours to complete

English

What you will learn

Students will learn how to develop concurrent software in Python and C/C++ programming languages.
Students will gain an introductory level of understanding of GPU hardware and software architectures.

Skills you will gain

Cuda
Python Programming
Thread (Computing)
C++

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 4 in the

GPU Programming Specialization

Beginner Level

Some experience programming

Approx. 19 hours to complete

English

Instructor

Chancellor Thomas Pascale

Instructor and Software Engineer

Computer Science

4,903 Learners

4 Courses

Syllabus - What you will learn from this course

Week1

Week 1

3 hours to complete

Course Overview

3 hours to complete

3 videos (Total 12 min), 4 readings, 1 quiz

Week2

Week 2

3 hours to complete

Core Principles of Parallel Programming on CPUs and GPUs

In order to create software that process greater amounts of data at faster speeds, software operating systems, programming languages, and frameworks require strategies for accessing and modification of data in a manner that maximizes speed, while minimizing the possibility of data being in incorrect states. In this module, students will be presented canonical concurrency problems such as the Dining Philosophers. Additionally, they will learn how operating systems and programming languages handle these problems, and discuss real world big data concurrency applications.

3 hours to complete

6 videos (Total 35 min), 3 readings, 2 quizzes

6 videos

Real World Concurrent Programming3mConcurrent Programming Pitfalls7m Concurrent Programming Problems and Algorithms Presentation6mOptional: Hungry Chickens Problem3mConcurrent Programming Patterns6mSerial Versus Parallel Code and Flynn's Taxonomy6m

3 readings

Optional Concurrent Programming Problems and Algorithms Resources15mOptional Parallel Programming Resources10mOptional Serial Versus Parallel Programming Resources15m

Week3

Week 3

6 hours to complete

Introduction to Parallel Programming with C and Python

6 hours to complete

6 videos (Total 31 min), 2 readings, 4 quizzes

6 videos

Lesson 1 Python 3 Parallel Programming Syntax and Patterns Presentation Video Lecture8mLesson 2 Python 3 Laboratory Project Structure Video Lecture5mLesson 2 Python 3 Assignment Project Structure Video Lecture3mLesson 3 C++ Parallel Programming Syntax And Patterns Video Lecture5mLesson 4 C++ Laboratory Project Structure Video Lecture5mLesson 4 C++ Assignment Project Structure Video Lecture2m

2 readings

Optional Python Parallel Programming Resources30mOptional C++ Parallel Programming Resources30m

2 practice exercises

Python 3 Parallel Programming Quiz10mC++ Parallel Programming Quiz10m

Week4

Week 4

3 hours to complete

NVidia GPU Hardware/Software

3 hours to complete

9 videos (Total 36 min), 1 reading, 3 quizzes

9 videos

Integrated versus Dedicated GPUs4mGUI CLI Tools for Identifying Installed GPU Hardware3mNvidia GPU Architectures3mCUDA Linux Installation3mCUDA Software Layers4mCUDA Code Compilation3mCUDA Help Lab and Assignment Walkthrough2mCUDA Runtime Driver APIs2mCUDA Driver and Runtime APIs Lab and Assignment Walkthrough7m

1 reading

Nvidia GTC Keynote Presentation10m

1 practice exercise

Nvidia Software and Hardware Quiz30m

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Can I program on my own laptop/desktop?
Yes, but you will need to update code files to the labs and assignments. For modules 4 and 5 you will need to have an Nvidia GPU installed on your machine. The in-browser environment for labs and assignments is built to allow for all required programming.

More questions? Visit the Learner Help Center.

Introduction to Concurrent Programming with GPUs

About this Course

What you will learn

Skills you will gain