Introduction to Concurrent Programming with GPUs
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Introduction to Concurrent Programming with GPUs
This course is part of GPU Programming Specialization
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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.
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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.
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 videos•Total 12 minutes
- GPU Programming Specialization Overview•3 minutes
- Course Expectations•3 minutes
- Coursera Lab Assignments Overview•6 minutes
4 readings•Total 80 minutes
- Course Overview•10 minutes
- Course Outline•10 minutes
- Introductory C++ Programming Resources•30 minutes
- VS Code and GitHub Resources•30 minutes
1 programming assignment•Total 30 minutes
- Introductory C++ Programming Assignment•30 minutes
2 discussion prompts•Total 30 minutes
- GPU Interest Discussion•15 minutes
- General Programming Experience Discussion•15 minutes
1 ungraded lab•Total 30 minutes
- Introductory C++ Programming Lab Exercise•30 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 videos•Total 35 minutes
- Real World Concurrent Programming•4 minutes
- Concurrent Programming Pitfalls•7 minutes
- Concurrent Programming Problems and Algorithms Presentation•7 minutes
- Optional: Hungry Chickens Problem•4 minutes
- Concurrent Programming Patterns•6 minutes
- Serial Versus Parallel Code and Flynn's Taxonomy•7 minutes
3 readings•Total 40 minutes
- Optional Concurrent Programming Problems and Algorithms Resources•15 minutes
- Optional Parallel Programming Resources•10 minutes
- Optional Serial Versus Parallel Programming Resources•15 minutes
2 programming assignments•Total 60 minutes
- Dining Philosophers Assignment•30 minutes
- Sleeping Barber Problem Assignment•30 minutes
1 discussion prompt•Total 15 minutes
- Flynn's Taxonomy Discussion•15 minutes
1 ungraded lab•Total 30 minutes
- Producer Consumer Laboratory Activity•30 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 videos•Total 31 minutes
- Python 3 Parallel Programming Syntax and Patterns Presentation•9 minutes
- Python 3 Laboratory Project Structure•6 minutes
- Python 3 Assignment Project Structure•3 minutes
- C++ Parallel Programming Syntax And Patterns•6 minutes
- C++ Laboratory Project Structure•6 minutes
- C++ Assignment Project Structure•2 minutes
2 readings•Total 60 minutes
- Optional Python Parallel Programming Resources•30 minutes
- Optional C++ Parallel Programming Resources•30 minutes
2 assignments•Total 20 minutes
- Python 3 Parallel Programming Quiz•10 minutes
- C++ Parallel Programming Quiz•10 minutes
2 programming assignments•Total 120 minutes
- Python 3 Parallel Programming Assignment•60 minutes
- C++ Parallel Programming Assignment•60 minutes
1 discussion prompt•Total 10 minutes
- Single Versus Multithreaded Programming Discussion•10 minutes
2 ungraded labs•Total 120 minutes
- Python 3 Parallel Programming Lab Activity•60 minutes
- C++ Parallel Programming Lab Activity•60 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 videos•Total 36 minutes
- Integrated versus Dedicated GPUs•5 minutes
- GUI CLI Tools for Identifying Installed GPU Hardware•3 minutes
- Nvidia GPU Architectures•4 minutes
- CUDA Linux Installation•4 minutes
- CUDA Help Lab and Assignment Walkthrough•3 minutes
- CUDA Software Layers•4 minutes
- CUDA Code Compilation•3 minutes
- CUDA Runtime Driver APIs•3 minutes
- CUDA Driver and Runtime APIs Lab and Assignment Walkthrough•8 minutes
1 reading•Total 10 minutes
- Nvidia GTC Keynote Presentation•10 minutes
1 assignment•Total 30 minutes
- Nvidia Software and Hardware Quiz•30 minutes
2 programming assignments•Total 75 minutes
- NVCC Help Command Assignment•15 minutes
- CUDA Software Layers and Compilation•60 minutes
1 discussion prompt•Total 10 minutes
- GUI/UI Tools for Identifying Installed GPU Hardware Discussion•10 minutes
1 ungraded lab•Total 30 minutes
- CUDA Compilation Lab•30 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.
What's included
6 videos1 reading1 assignment2 programming assignments1 discussion prompt1 ungraded lab
6 videos•Total 28 minutes
- CUDA Compilation and Execution Syntax•7 minutes
- CUDA Keywords Video•3 minutes
- Simple CUDA Lab and Assignment Walkthrough•4 minutes
- CUDA IDE Programming•4 minutes
- CUDA Project Structure and Best Practices•7 minutes
- Complex CUDA Project Assignment Walkthrough•2 minutes
1 reading•Total 30 minutes
- Optional NVidia CUDA Programming Resources•30 minutes
1 assignment•Total 10 minutes
- GPU Programming Quiz•10 minutes
2 programming assignments•Total 120 minutes
- Simple CUDA Project Assignment•60 minutes
- Complex CUDA Project Assignment•60 minutes
1 discussion prompt•Total 10 minutes
- End of Course Discussion•10 minutes
1 ungraded lab•Total 60 minutes
- Simple CUDA code Lab•60 minutes
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Frequently asked questions
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.
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.
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.
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Financial aid available,