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.
Introduction to Concurrent Programming with GPUs
Introduction to Concurrent Programming with GPUs
This course is part of GPU Programming Specialization
Instructor: Chancellor Thomas Pascale
Access provided by Foundation University Islamabad
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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
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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.
Skills you'll gain
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Assessments
4 assignments
Taught in English
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This course is part of the GPU Programming Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
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
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
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
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
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
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