Learn Cuda 2024 – Best CUDA courses & Best CUDA tutorials & Best CUDA Books

Best Cuda Courses 2021

 

Best Cuda Tutorials 2021

CUDA programming Masterclass

This course is about CUDA programming. We’ll start our discussion by looking at basic concepts, including the CUDA programming model, the execution model, and the memory model. Next, we will show you how to implement advanced algorithms using CUDA. CUDA programming is all about performance. So, throughout this course, you will learn several optimization techniques and how to use them to implement algorithms. We will also discuss in detail about profiling techniques and some of the tools including nvprof, nvvp, CUDA Memcheck, CUDA-GDB tools in CUDA toolkit. This course contains the following sections.

Introduction to CUDA Programming and the CUDA Programming Model
CUDA runtime model
CUDA Memory Model – Global Memory
CUDA Memory Model – Shared and Constant Memory
CUDA flow
Setting CUDA instruction level primitives
Algorithm implementation with CUDA
CUDA Tools

With this course we also include lots of programming exercises and quizzes. Answering all of these questions will help you digest the concepts we are discussing here.

This course is the first in the CUDA masterclass series we are currently working on. So the knowledge you gain here is essential to take these courses as well.

Learning CUDA 10 Programming

Want to write GPU-accelerated apps, but don’t know where to start? With CUDA 10, you can easily add GPU processing to your C and C ++ projects. CUDA 10 is the de facto framework used to develop high performance GPU accelerated applications.

In this course, you will be introduced to CUDA programming through practical examples. CUDA provides a versatile programming model that gives you access to the enormous computing power of modern GPUs, as well as powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms.

After completing this course, you will understand the basics of CUDA programming and can start using it immediately in your applications.

Best CUDA Books 2021

CUDA by Example: An Introduction to General-Purpose GPU Programming

CUDA by Example: An Introduction to General-Purpose GPU Programming by Jason Sanders and Edward Kandrot. CUDA is a computer architecture designed to facilitate the development of parallel programs. Combined with a comprehensive software platform, the CUDA architecture enables programmers to harness the immense power of graphics processing units (GPUs) when creating high-performance applications. GPUs, of course, have long been available for demanding graphics and gaming applications. CUDA now offers this valuable resource to programmers working on applications in other fields, such as science, engineering, and finance. No graphical programming knowledge is required, just the ability to program in a slightly extended version of C.

Written by two senior members of the CUDA software platform team, shows programmers how to use this new technology. The authors present each area of ​​CUDA development through practical examples. After a concise introduction to the CUDA platform and architecture, along with a CUDA C quick start guide, the book details the techniques and trade-offs associated with each key CUDA functionality. You will learn when to use each CUDA C extension and how to write CUDA software that offers truly exceptional performance.

The main topics covered include

Parallel programming
Wired cooperation
Memory and constant events
Texture memory
Graphical interoperability
Atomic
Currents
CUDA C on multiple GPUs
Advanced atomic
Additional CUDA Resources

Professional CUDA C Programming

Professional CUDA C Programming by John Cheng, Max Grossman and Ty McKercher. Enter the powerful world of parallel GPU programming with this practical and realistic guide. Designed for professionals in multiple industries, CUDA C Professional Programming introduces CUDA, a parallel computing platform and programming model designed to facilitate the development of GPU programming, the basics in an easy-to-follow format, and teaches readers how to think in parallel and Implement parallel algorithms on GPU. Each chapter covers a specific topic and includes practical examples that illustrate the development process, allowing readers to explore the “hard” and “soft” aspects of GPU programming.

Computing architectures are undergoing a fundamental shift towards scalable parallel computing driven by the demands of applications in industry and science. This book demonstrates the challenges of efficiently utilizing computational resources at peak performance, introduces modern techniques to address these challenges, while increasing accessibility for professionals who are not necessarily experts in parallel programming. The CUDA programming model and tools allow developers to write high-performance applications on a scalable parallel computing platform – the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. CUDA Recognized Authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in professional CUDA C programming, including:

CUDA programming model
GPU runtime model
GPU memory model
Streams, events and concurrency
Multi-GPU programming
CUDA domain specific libraries
Profiling and performance tuning

The book makes the complex concepts of CUDA easy to understand for anyone with basic software development knowledge with exercises designed to be readable and effective. For the professional seeking access to parallel computing and the high-performance computing community, Professional CUDA C Programming is an invaluable resource, with the latest information available on the market.

Programming Massively Parallel Processors: A Hands-on Approach 3rd Edition

Programming Massively Parallel Processors: A Hands-on Approach 3rd Edition by David B. Kirk and Wen-mei W. Hwu introduces both students and professionals to the basics of parallel programming and GPU architecture, exploring, in detail, various techniques for building parallel programs.

The case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics related to performance, floating point formatting, parallel models, and dynamic parallelism are covered in depth.

For this new edition, the authors updated their coverage of CUDA, including coverage of new libraries, such as CuDNN, moved content that has become less important to the appendices, added two new chapters on parallel models, and placed updated case studies to reflect the current industry. the practices.

Bestsellers