Description
Learn CUDA with Docker! This comprehensive course teaches you how to program in CUDA without the need for expensive NVIDIA graphics cards, using Docker tools and emulators. This innovative course offers a much-anticipated approach to learning CUDA programming without the need for dedicated NVIDIA graphics cards. Finally, enthusiasts can easily learn CUDA topics on their laptop, tablet, or even their mobile phone. CUDA technology provides a general-purpose programming model that gives users access to the incredible computing power of modern GPUs, as well as powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms. This course will demonstrate how to use Docker, OS-level virtualization, and GPU cycle simulators in a practical way to teach parallel architecture and the NVIDIA programming model in a smooth and understandable way. In addition, the course is complemented by live online classes and topics related to parallel, distributed computing, and high-performance computing software systems. In addition to access to video content, participants will also benefit from the interactive features of specialized scientific programming platforms to take their skills in implementing parallel algorithms to the highest level. This training is a smooth path for developers who want to enter the world of heavy computing and parallel processing and optimize their projects without getting involved in heavy hardware costs.
What you will learn
- Programming without a graphics card: Learn how to code with CUDA without the need for expensive GPU hardware.
- Docker and virtualization concepts: Introduction to Docker principles, operating system-level virtualization, and the use of containers.
- Introduction to graphics fundamentals: Basic understanding of graphics processors and parallel processing architectures.
- Installing and setting up tools: Learn how to install the CUDA environment and its related tools.
- Managing threads and blocks: Exploring how CUDA threads and blocks work in various combinations.
- Implementing parallel algorithms: Ability to design and build efficient parallel algorithms using sample codes.
- High-performance computing: Introduction to heavy computing software stacks, distributed systems, and task management tools.
This course is suitable for people who:
- Hardware-less developers: People who are interested in learning CUDA programming but don’t have access to expensive graphics cards.
- C and C++ programmers: Those who have basic programming knowledge and want to enter the field of parallel computing.
- Researchers and students in the field of artificial intelligence: Researchers working in the fields of machine learning, image processing, and heavy algorithms.
- Fast processing enthusiasts: People who are interested in learning modern software technologies and high-performance processing systems.
Learn CUDA with Docker! course details
- Publisher: Udemy
- Instructor: Scientific Programmer™ Team , Scientific Programming School
- Training level: Beginner to advanced
- Training duration: 2 hours and 58 minutes
- Number of lessons: 44
Course headings
Prerequisites for the Learn CUDA with Docker! course
- Basic C or C++ programming knowledge
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: English
Quality: 720p
Download link
Rapidgator link
File(s) password: www.downloadly.ir
File size
798 MB