GPU Computing with CUDA and Python

GPU Computing with CUDA and Python


Become an expert of Multi-GPU programming with CUDA. CuPy & NVLink

Classes : 30                  Days : 3 months                  Duration : Weekdays / Weekends

The aim of this course is to provide the basics of the architecture of a graphics card and allow a first approach to CUDA programming by developing simple examples with a growing degree of difficulty. Students get to know the fundamental concepts of Parallel Computing and GPU programming with CUDA. It is a parallel computing platform and an API model, developed by Nvidia.

In addition to graphical rendering, GPU-driven parallel computing is used for scientific modelling, machine learning, and other parallelization-prone jobs today.

In addition to TensorFlow, many other deep learning frameworks rely on CUDA for their GPU support, including Caffe2, Chainer, Databricks,, Keras, MATLAB, MXNet, PyTorch, Theano, and Torch.

Self-driving cars, machine learning and augmented reality are some of the examples of modern applications that involve parallel computing.

In case you are a scientist working with NumPy and SciPy, the easiest way to optimize your code for GPU computing is to use CuPy.

With the availability of high performance GPUs and a language, such as CUDA, which greatly simplifies programming, everyone can have at home and easily use a supercomputer.

Experts from the field of Maths, Data Science and Management, each with over 25 years of International experience working in EU/US/Australia

Who this course is for:

People aiming at a first approach to parallel programming on GPUs.

:- Experience with C/C++ and Python
:- You have theoretical knowledge of TensorFlow platform
:- You have a genuine interest in CUDA computing

:- How GPU computing works
:- NVIDIA hardware and CUDA
:- Parallelization Paradigms
:- Host and Device parts of code
:- Threads, blocks, Core, warps, SIMT
:- GPU memory management
:- High level applications and CUDA
:- CUDA Program Flow
:- CUDA memories
:- CUDA Profiling and Visual Profiler
:- Coalesced Memory and Adjacent differences
:- 2-D grids
:- Pycuda, CuPy to call CUDA from python
:- Jupyter note for python scripts
:- NVLink programming


Fabulous NLP + ML course

I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.


Excellent ML course!

The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.

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