CUDA GPU Programming Beginner To Advanced

CUDA GPU Programming Beginner To Advanced, available at $24.99, has an average rating of 3.55, with 12 lectures, based on 44 reviews, and has 250 subscribers.

You will learn about Basic to advanced level concepts of Parallel Computing and GPU Programming with CUDA Practical exercises along the way for you to practice your new CUDA skills Learn about the range of GPUs available in the market and how to benchmark them Research areas of GPU Programming Theoretical knowledge of GPU programming and parallel computing Experience analyzing research papers in the field of parallel computing Theoretical and programming experience with other parallel programming libraries and frameworks like open MP and mpi Much more This course is ideal for individuals who are Anyone who wants to learn GPU programming with CUDA in a practical hands-on manner It is particularly useful for Anyone who wants to learn GPU programming with CUDA in a practical hands-on manner.

Enroll now: CUDA GPU Programming Beginner To Advanced

Summary

Title: CUDA GPU Programming Beginner To Advanced

Price: $24.99

Average Rating: 3.55

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Basic to advanced level concepts of Parallel Computing and GPU Programming with CUDA

Practical exercises along the way for you to practice your new CUDA skills

Learn about the range of GPUs available in the market and how to benchmark them

Research areas of GPU Programming

Theoretical knowledge of GPU programming and parallel computing

Experience analyzing research papers in the field of parallel computing

Theoretical and programming experience with other parallel programming libraries and frameworks like open MP and mpi

Much more

Who Should Attend

Anyone who wants to learn GPU programming with CUDA in a practical hands-on manner

Target Audiences

Anyone who wants to learn GPU programming with CUDA in a practical hands-on manner

THE BEST CUDA GPU PROGRAMMING COURSE FOR TAKING STUDENTS FROM BEGINNER TO ADVANCED 

The primary goal of this course is to teach students the fundamental concepts of Parallel Computing and GPU programming with CUDA (Compute Unified Device Architecture)

The course is designed to help beginning programmers gain theoretical knowledge as well as practical skills in GPU programming with CUDA to further their career.

Everything is covered step by step.

YOU WILL LEARN:

The background of GPU programming

NVIDIA GPUs for General Purpose and their Application Areas

CUDA Memory Models

CUDA Functional Pipeline

Programming Pipeline & CUDA Toolkit

Parallelism Models (mpi, open MP, CUDA)

CUDA Performance Benchmarking

Much more

Throughout the course, I will give you practical exercisesfor you to test out your new CUDA knowledge and programming skills.

When you are finishedwith this course, you will have laid the foundation for your futureCUDA GPU Programming job or promotion with your new GPU programming skills.

I look forward to meeting you in the course forum where I’ll be available to help you along the way and answer questions that you might have.

WHAT IS CUDA & GPU PROGRAMMING?

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by Nvidia.

It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general-purpose processing — an approach termed GPGPU (General-Purpose computing on Graphics Processing Units).

The CUDA platform is a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels.

GPU programming enables GPUs to be used in scientific computing. GPUs were supposed to be developed for the dedicated purpose of graphics support. But, with the discovery of the ability of GPUs in number crunching.

It’s become mainstream to use GPUs for scientific application development.

TOP 3 BENEFITS OF LEARNING GPU PROGRAMMING WITH CUDA

1: High demand.There is a high demand for skilled GPU programmers with CUDA.

2: A usable skill. With GPU programming skills you can program GPUs to solve complex and computationally intensive tasks swiftly. GPU programming is the skill used in almost all fields of engineering and computer sciences in one way or the other.

3: Further your career. Software companies all around the world are actively seeking out, competent GPU programmers. There are not a lot of them, so the pay is good. If you learn GPU programming, a promotion or a new job is a likely outcome.

FREQUENTLY ASKED QUESTIONS

Do I need my own GPU for this course? 
No, you can use cloud-based solutions. You don’t have to purchase the hardware. Even If you don’t want to purchase cloud-based GPU environment. You can still take this course to get theoretical knowledge of CUDA and programming experience of other open-source libraries like mpi/openMP

What’s the difference in this course from other CUDA courses? 
Along with hands-on GPU programming skills, you also get in-depth theoretical knowledge.

The course exposes you to cutting edge research fields in which GPU programming is in use these days.

Application development using CUDA alone is rare. This course also gives you programming experience with open-source parallel libraries like Open MP/mpi.(i.e. Hybrid Parallelism)

GUARANTEE

If within 30 days of buying the course you decide that it’s not for you, please get a refund. We only want happy students.

ARE YOU READY TO LEARN CUDA DIGITAL PROGRAMMING?

Please press the “Take This Course” button and start learning 2 minutes from now!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Background of GPU programming

Lecture 2: Introduction to GPU programming

Chapter 2: CUDA

Lecture 1: NVIDIA CUDA for Parallel Computing and their application areas

Lecture 2: Key concepts

Lecture 3: CUDA Memory Models

Lecture 4: CUDA Functional Pipeline

Lecture 5: Programming Pipeline & CUDA Toolkit

Lecture 6: Matrix Multiplication Example

Lecture 7: CUDA Performance Benchmarking

Lecture 8: Parallelism Models (mpi, open MP, CUDA)

Lecture 9: CUDA Toolkit Samples Overview

Chapter 3: Conclusion

Lecture 1: Conclusion

Instructors

The Startup Central Co.
World Class IT Education

Muhammad Adil
Computational Engineer

Rating Distribution

1 stars: 7 votes

2 stars: 6 votes

3 stars: 11 votes

4 stars: 9 votes

5 stars: 11 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!