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CUDA GPU Programming Beginner To Advanced

2 hours on-demand video

$ 12.99

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Brief Introduction

Learn CUDA programming and parallel computing with my simple and straightforward cuda programming masterclass

Description

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 exercises for you to test out your new CUDA knowledge and programming skills.

When you are finished with this course, you will have laid the foundation for your future CUDA 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!

Requirements

Requirements
Basic knowledge of programming. Preferably in C/C++
Must have a computer with NVIDIA GPU (You can also use NVIDIA Cloud) if you want to get programming experience. If you don’t have a GPU, still you can get theoretical knowledge of CUDA and programming experience of Open source parallel programming libraries like OpenMP/mpi

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