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GPU Programming with C++ and CUDA

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GPU Programming with C++ and CUDA

Instructor: Packt - Course Instructors

Included with

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10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Accelerate real-world tasks using GPU parallelism
Optimize performance with CUDA streams and custom C++ solutions
Create and share GPU libraries with Python integration

Details to know

Shareable certificate

Add to your LinkedIn profile

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There are 10 modules in this course

In this course, you’ll master GPU programming using C++ and CUDA to significantly enhance your software's performance. By focusing on parallelism, you’ll learn to leverage the full power of GPUs for high-performance computing applications.

You will acquire practical knowledge on managing GPU devices, optimizing GPU resource usage, and integrating GPU code with Python to build scalable and efficient applications. This course emphasizes real-world strategies for optimizing performance and building reusable libraries. This course combines fundamental theory with hands-on applications to help you solve complex performance challenges. You'll not only understand the core concepts but also implement them in real-world projects, such as creating libraries for Python integration. Ideal for C++ developers with experience in basic programming concepts, this course will take you through advanced topics, from parallel algorithms to multi-GPU usage. A background in operating systems is recommended for tackling more complex concepts. Based on the book, GPU Programming with C++ and CUDA, by Paulo Motta.

In this section, we explore parallelism in software, its importance, and the differences between CPU and GPU architectures to build a foundation for GPU programming.

What's included

2 videos4 readings1 assignment

2 videos Total 2 minutes

Introduction - Overview Video 1 minute
Introduction to Parallel Programming - Overview Video 1 minute

4 readings Total 40 minutes

Introduction 10 minutes
Why Is Parallelism Important? 10 minutes
An Overview of GPU Architecture 10 minutes
Memory Management and Access 10 minutes

1 assignment Total 10 minutes

Parallel Programming Fundamentals 10 minutes

In this section, we configure a GPU environment using Docker, locate official Linux documentation, and install the CUDA toolkit on Ubuntu 20.04 or 22.04 for AI and machine learning workflows.

What's included

1 video3 readings1 assignment

In this section, we introduce GPU programming fundamentals, including kernel execution, device inspection, and setting up a working environment for CUDA development.

What's included

1 video4 readings1 assignment

In this section, we explore SIMD execution, data movement, and parallel vector addition for GPU programming.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Hello Again, but in Parallel - Overview Video 1 minute

5 readings Total 50 minutes

Introduction 10 minutes
Not-So-Parallel Prime Number Verification 10 minutes
A Kernel to Test for Prime Numbers 10 minutes
How to Measure Execution Time on the GPU 10 minutes
Vector Addition 10 minutes

1 assignment Total 10 minutes

Parallel Computing Fundamentals 10 minutes

In this section, we explore GPU thread, block, and grid configurations, asynchronous data transfer, streams, events, and shared memory to optimize performance in parallel computing.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

A Closer Look into the World of GPUs - Overview Video 1 minute

5 readings Total 70 minutes

Introduction 20 minutes
Putting It All Together 10 minutes
Asynchronous Data Transfers 10 minutes
Parallelizing with Streams 20 minutes
Following the Events 10 minutes

1 assignment Total 10 minutes

Exploring GPU Architecture and Optimization Techniques 10 minutes

In this section, we explore parallel algorithm design, focusing on matrix operations, reduction, and workload balancing for efficient GPU execution.

What's included

1 video8 readings1 assignment

1 video Total 1 minute

Parallel Algorithms with CUDA - Overview Video 1 minute

8 readings Total 80 minutes

Introduction 10 minutes
Understanding How to Spot and Exploit Parallelism 10 minutes
Balancing the Workloads 10 minutes
Computing Matrix Addition and Multiplication 10 minutes
Calculating Numerical Integrals 10 minutes
Reducing from Many 10 minutes
Sorting Data 10 minutes
Processing Sensor Data with a Convolution 10 minutes

1 assignment Total 10 minutes

Data Management and Parallel Execution 10 minutes

In this section, we explore GPU optimization and profile with NVIDIA Nsight Compute.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Performance Strategies - Overview Video 1 minute

5 readings Total 100 minutes

Introduction 10 minutes
Profiling with NVIDIA Nsight Compute 20 minutes
Optimizing to Speed Up Our Code 20 minutes
Using the Release Configuration 20 minutes
Using Loop Unroll for Further Improvements 30 minutes

1 assignment Total 10 minutes

Performance Strategies in Software Development 10 minutes

In this section, we explore debugging CUDA code with VS Code, using CUDA streams to overlap memory and kernel operations, and configuring multiple GPUs for parallel processing.

What's included

1 video4 readings1 assignment

In this section, we explore methods to integrate C++ GPU code with Python, focusing on Ctypes, custom wrappers, and performance analysis for efficient cross-language execution.

What's included

1 video4 readings1 assignment

In this section, we explore GPU development using cuBLAS and Thrust, optimize code for memory and thread efficiency, and test with GTest and Pytest to ensure reliability and performance.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Exploring Existing GPU Models - Overview Video 1 minute

5 readings Total 50 minutes

Exploring Existing GPU Models 10 minutes
Using Thrust to Write GPU Code 10 minutes
Moving Sequential Code to the GPU 10 minutes
Testing Your Code with GTest and Pytest 10 minutes
Using Pytest with Our Code 10 minutes

1 assignment Total 10 minutes

GPU Programming Fundamentals 10 minutes

Instructor

Packt - Course Instructors

Packt

1,608 Courses 446,194 learners

Offered by

Packt

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

GPU Programming with C++ and CUDA

GPU Programming with C++ and CUDA

What you'll learn

Details to know

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

There are 10 modules in this course

Introduction to Parallel Programming

What's included

Setting Up Your Development Environment

What's included

Hello CUDA

What's included

Hello Again, but in Parallel

What's included

A Closer Look into the World of GPUs

What's included

Parallel Algorithms with CUDA

What's included

Performance Strategies

What's included

Overlaying Multiple Operations

What's included

Exposing Your Code to Python

What's included

Exploring Existing GPU Models

What's included

Instructor

Offered by

Explore more from Software Development

Introduction to Parallel Programming with CUDA

Multicore and GPGPU Programming

Introduction to Concurrent Programming with GPUs

OpenCL Programming

Why people choose Coursera for their career

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

Can I preview a course before enrolling?

When will I have access to the lectures and assignments?

What will I get when I enroll?

More questions