Introduction to GPU computing with CUDA
What you'll learn
- Understanding the basics of parallel programming on GPU.
- Understanding the basics of GPU architecture.
- Writing simple programs in CUDA language.
Course content
- Preview05:25
- Preview03:18
- Preview02:20
- 03:52Heterogeneous Computing and NVIDIA Compiler Driver
- 06:30CUDA Download Installation and IDEs
Requirements
- Experience with C/C++
Description
Self-driving cars, machine learning and augmented reality are some of the examples of modern applications that involve parallel computing.
With the availability of high performance GPUs and a language, such as CUDA, which greatly simplifies programming, everyone can have at home and easily use a supercomputer.
The aim of this course is to provide the basics of the architecture of a graphics card and allow a first approach to CUDA programming by developing simple examples with a growing degree of difficulty.
Who this course is for:
- People aiming at a first approach to parallel programming on GPUs.
Instructor
![Orange Owl](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 3.2 Instructor Rating
- 124 Reviews
- 624 Students
- 1 Course
We consult on and develop software for solving challenging technical computing problems in scientific or industrial areas.
We have a 10+ years experience in solving scientific problems by using the appropriate numerical techniques.
Based on our experience on programming parallel architectures, we face computationally intensive problems, optimizing our algorithms for running on multi-core computers, Graphics Processing Units (GPUs) or hybrid resources.