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.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

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.

Introduction to Concurrent Programming with GPUs

Introduction to Concurrent Programming with GPUs

This course is part of GPU Programming Specialization

Instructor: Chancellor Thomas Pascale

21,907 already enrolled

Included with

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

80%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

80%

Most learners liked this course

What you'll 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'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the GPU Programming Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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.

Module details

The purpose of this foundational module is to prepare students for success within the GPU Programming Specialization. To this end, the module presents the course's overall goals and technical expectations and guides utilizing the Coursera lab environment. Students will conclude by completing an introductory C++ programming assignment to ensure they are equipped for the curriculum.

What's included

3 videos4 readings1 programming assignment2 discussion prompts1 ungraded lab

3 videosTotal 12 minutes

GPU Programming Specialization Overview3 minutes
Course Expectations3 minutes
Coursera Lab Assignments Overview6 minutes

4 readingsTotal 80 minutes

Course Overview10 minutes
Course Outline10 minutes
Introductory C++ Programming Resources30 minutes
VS Code and GitHub Resources30 minutes

1 programming assignmentTotal 30 minutes

Introductory C++ Programming Assignment30 minutes

2 discussion promptsTotal 30 minutes

GPU Interest Discussion15 minutes
General Programming Experience Discussion15 minutes

1 ungraded labTotal 30 minutes

Introductory C++ Programming Lab Exercise30 minutes

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.

What's included

6 videos3 readings2 programming assignments1 discussion prompt1 ungraded lab

6 videosTotal 35 minutes

Real World Concurrent Programming4 minutes
Concurrent Programming Pitfalls7 minutes
Concurrent Programming Problems and Algorithms Presentation7 minutes
Optional: Hungry Chickens Problem4 minutes
Concurrent Programming Patterns6 minutes
Serial Versus Parallel Code and Flynn's Taxonomy7 minutes

3 readingsTotal 40 minutes

Optional Concurrent Programming Problems and Algorithms Resources15 minutes
Optional Parallel Programming Resources10 minutes
Optional Serial Versus Parallel Programming Resources15 minutes

2 programming assignmentsTotal 60 minutes

Dining Philosophers Assignment30 minutes
Sleeping Barber Problem Assignment30 minutes

1 discussion promptTotal 15 minutes

Flynn's Taxonomy Discussion15 minutes

1 ungraded labTotal 30 minutes

Producer Consumer Laboratory Activity30 minutes

Modern programming languages allow developers to create software with complex logic for manipulation of data in parallel, taking advantage of the multiple CPU cores in most computers. Students will develop simple software, written in the C++ and Python 3 programming languages, that process data sets concurrently.

What's included

6 videos2 readings2 assignments2 programming assignments1 discussion prompt2 ungraded labs

6 videosTotal 31 minutes

Python 3 Parallel Programming Syntax and Patterns Presentation9 minutes
Python 3 Laboratory Project Structure6 minutes
Python 3 Assignment Project Structure3 minutes
C++ Parallel Programming Syntax And Patterns6 minutes
C++ Laboratory Project Structure6 minutes
C++ Assignment Project Structure2 minutes

2 readingsTotal 60 minutes

Optional Python Parallel Programming Resources30 minutes
Optional C++ Parallel Programming Resources30 minutes

2 assignmentsTotal 20 minutes

Python 3 Parallel Programming Quiz10 minutes
C++ Parallel Programming Quiz10 minutes

2 programming assignmentsTotal 120 minutes

Python 3 Parallel Programming Assignment60 minutes
C++ Parallel Programming Assignment60 minutes

1 discussion promptTotal 10 minutes

Single Versus Multithreaded Programming Discussion10 minutes

2 ungraded labsTotal 120 minutes

Python 3 Parallel Programming Lab Activity60 minutes
C++ Parallel Programming Lab Activity60 minutes

In order to write effective GPU-accelerated software, a developer must first understand the underlying hardware architecture and the software ecosystem that enables it. In this module, students will be presented with the key hardware architectures of NVidia GPUs. Additionally, they will learn about the CUDA software layers, the installation and compilation process, and the core differences between the Runtime and Driver APIs.

What's included

9 videos1 reading1 assignment2 programming assignments1 discussion prompt1 ungraded lab

9 videosTotal 36 minutes

Integrated versus Dedicated GPUs5 minutes
GUI CLI Tools for Identifying Installed GPU Hardware3 minutes
Nvidia GPU Architectures4 minutes
CUDA Linux Installation4 minutes
CUDA Help Lab and Assignment Walkthrough3 minutes
CUDA Software Layers4 minutes
CUDA Code Compilation3 minutes
CUDA Runtime Driver APIs3 minutes
CUDA Driver and Runtime APIs Lab and Assignment Walkthrough8 minutes

1 readingTotal 10 minutes

Nvidia GTC Keynote Presentation10 minutes

1 assignmentTotal 30 minutes

Nvidia Software and Hardware Quiz30 minutes

2 programming assignmentsTotal 75 minutes

NVCC Help Command Assignment15 minutes
CUDA Software Layers and Compilation60 minutes

1 discussion promptTotal 10 minutes

GUI/UI Tools for Identifying Installed GPU Hardware Discussion10 minutes

1 ungraded labTotal 30 minutes

CUDA Compilation Lab30 minutes

The purpose of this module is to transition from theory to practice by introducing the fundamentals of GPU programming. Students will learn the essential CUDA software keywords and syntax required for writing device code. They will also be guided through best practices for structuring both simple and complex projects, applying these concepts in hands-on assignments within common development environments.