Free and Comprehensive Course on NVIDIA CUDA Suit
Free and Comprehensive Course on NVIDIA CUDA Suit : Complete NVIDIA GPU Development SUIT
Updated on Jul, 2024
Language - English
Artificial Intelligence,Machine Learning
Lectures -12
Duration -1.5 hours
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Course Description
Unlock the full potential of NVIDIA GPUs with our comprehensive and free course on the NVIDIA CUDA Suite. This course is meticulously designed to cater to both beginners and experienced developers who are eager to delve into the world of parallel computing and GPU acceleration.
The NVIDIA CUDA Suite is a powerful platform for parallel computing that leverages the immense processing power of NVIDIA GPUs. Our course will guide you through the fundamentals of CUDA programming, starting with the basics and gradually advancing to more complex concepts. You will learn how to write efficient and optimized code that can significantly speed up computational tasks by harnessing the power of GPU parallelism.
Throughout the course, you will gain hands-on experience with real-world applications and examples. We will cover essential topics such as memory management, kernel functions, and performance optimization techniques. Additionally, you will explore advanced features like CUDA streams, events, and multi-GPU programming, which are crucial for developing high-performance applications.
Our course also includes detailed modules on integrating CUDA with popular programming languages like C++ and Python, making it easier for you to apply your newfound skills in various development environments. By the end of this course, you will have a solid understanding of the NVIDIA CUDA Suite and be well-equipped to tackle complex computational problems with ease.
Join us on this exciting journey to master GPU development with the NVIDIA CUDA Suite. Whether you are a student, researcher, or professional developer, this course will provide you with the knowledge and skills needed to excel in the rapidly evolving field of parallel computing. Enroll now and take the first step towards becoming an expert in GPU programming!
Goals
- GPU Architecture and CUDA Basics: Understand GPU architecture and write basic CUDA programs.
- Memory Management: Learn efficient memory allocation and data transfer techniques.
- Installation of NVIDIA CUDA Suit on Various Cloud like AWS, GCP and Azure.
- Performance Optimization: Apply advanced techniques to enhance CUDA application performance.
- Advanced CUDA Features and Libraries: Explore dynamic parallelism, unified memory, and CUDA libraries like cuBLAS and Thrust.
- Debugging and Profiling: Use tools to debug and profile CUDA applications, fixing common errors and optimizing code.
- Real-world Applications: Study applications and case studies in fields like machine learning and scientific computing.
- Hands-on Projects and Capstone: Implement a capstone project to apply and showcase your CUDA programming skills.
- CUDA Kernels and Parallel Programming: Master writing and optimizing CUDA kernels and handling parallel programming concepts.
Prerequisites
- Basic Programming Knowledge: Familiarity with programming concepts and experience in languages like C or C++.
- Understanding of Parallel Computing: Basic understanding of parallel computing principles, though not mandatory, is beneficial.
- Mathematical Foundations: A good grasp of mathematical concepts, particularly linear algebra and calculus.
- Fundamental Computer Science Concepts: Knowledge of fundamental computer science concepts such as algorithms and data structures.
- Access to Compatible Hardware: Access to a system with an NVIDIA GPU that supports CUDA.
- Development Environment Setup: Ability to set up a development environment, including installing the CUDA toolkit and necessary libraries.
- Familiarity with Linux or Windows: Basic familiarity with operating systems like Linux or Windows for development purposes.
- Eagerness to Learn: A strong willingness to learn and experiment with new concepts in GPU programming.
Curriculum
Check out the detailed breakdown of what’s inside the course
Free and Comprehensive Course on NVIDIA CUDA Suit
12 Lectures
-
NVIDIA Course Overview and Objectives
08:27
08:27
-
Introduction to NVIDIA CUDA SUIT
05:32
05:32
-
Overview of NVIDIA GPU Development Ecosystem
05:57
05:57
-
CUDA Toolkit Comprehensive GPU Programming Environment
07:16
07:16
-
Setup and Installation of NVIDIA CUDA Suit ON GCP
09:09
09:09
-
Setup and Installation of NVIDIA CUDA Suit ON Azure
09:44
09:44
-
NVIDIA Nsight Compute: Integrated Development and Debugging Tools
11:03
11:03
-
NVIDIA Nsight System: GPU-Accelerated Deep Learning Libraries
06:09
06:09
-
NVIDIA Utilities
09:39
09:39
-
NVIDIA System Management Interface (NVIDIA SMI)
08:16
08:16
-
CUDA Utilities
06:45
06:45
-
Nsight Compute Visual Studio Code Edition
06:30
06:30
Instructor Details
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