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 Yale

20,500 already enrolled

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

79%

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

79%

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

Python Programming

Details to know

Shareable certificate

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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

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.