CUDA Programming on Python

In this tutorial, I’ll show you everything you need to know about CUDA programming so that you could make use of GPU parallelization, thru simple modifications of your already existing code, running on a boring CPU. The following tutorial was recorded on NVIDIA’s Jetson Orin supercomputer. CUDA stands for Compute Unified Device Architecture, and is a parallel computing platform and application programming interface that enables software to use certain types of graphics processing units for general purpose processing, an approach called general-purpose computing on GPUs.

First, I will start by writing a simple function that does a vector multiplication, which is going to run on a CPU. Then we get the same job done using CUDA parallelization on a GPU. Keep in mind that GPU’s have more cores than CPU and hence when it comes to parallel computing of data, GPUs perform exceptionally better than CPUs even though GPUs have lower clock speed and lack several core management features as compared to CPUs. An example reveals that running 64 million massive multiplications on a GPU takes about 0.64 seconds, as opposed to 31.4 seconds when running on a CPU. This translates to a x50 gain in terms of speed, thanks to the parallelization on such a huge number of cores. Amazing ! This means that running a complex program on CPU taking about a month, could be executed in 14 hrs. This could be also faster given more cores.

Then, I’ll show you the gains in filling arrays on python on a CPU vs on a GPU. Another example reveals that the amount of time it took to fill the array on a CPU is about 2.58 seconds, as opposed to 0.39 seconds on a GPU, which is a gain of about 6.6x. The last fundamental section of this video is to show the gains in rendering images (or videos) on python. We will demonstrate why you see some film producers or movie makers rendering and editing their content on a GPU. GPU rendering delivers with a graphics card rather of a CPU, which may substantially speed up the rendering process because GPUs are primarily built for fast picture rendering. GPUs were developed in response to graphically intensive applications that taxed CPUs and slowed processing speed. I will use the Mandelbrot set to perform a comparison between CPU and GPU power. This example reveals that only 1.4 seconds of execution is needed on a GPU as opposed to 110 seconds on a CPU, which is a 78x gain. This simply means that instead of rendering a 4K resolution video over a week on a CPU, you could get the same video in 8K resolution rendered in 2 hours on a GPU, if you are using 32 threads. So imagine if you doubled the threads and blocks involved in GPU optimization.

⏲Outline⏲

00:00 Introduction
00:33 Multiplication gains on GPUs vs CPUs
08:31 Filling an array on GPUs vs CPUs
11:55 Rendering gains on GPU vs CPU
12:35 What is a Mandelbrot set ?
13:39 Mandelbrot set rendering on CPU
17:01 Mandelbrot set rendering on GPU
20:54 Outro

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#cuda #cudaprogramming #gpu

[aishahoura]

Thank you so much for responding to my request for making a CUDA programming. I have donated 0.1 BTC to your account as a way to thank you. My professor has done so many hours trying to explain CUDA and none of my classmates really understood. I just can not believe that you do all this for free and that is why me and my classmates have decided to collect some funds to donate to you.

Thanks for all that you do and please keep going.

[Drex.Yt]

Too hard to find high-quality content like this these days. Thank you so much

[denizart]

You just opened my eyes to parallel programming. Thanks for the quick overview.

[aoungamingyt]

Thank you so much. Probably the best introdution to CUDA with Python. The example you use, while very basic, touches on usage of blocks, which is usually omitted in other introduction-level tutorials. Great stuff! Hope you return with some more videos. I have subscribed!

[sksk-lokc]

That was very well explained. I have only have taken one course, and you made it clearer than my professor or fellow students ever did.

[tatldunyas]

Just did my research and this guy is at one of the most prestigious universities in the world ! No wonder why his lectures come up neat !

[apogeetheboss]

as a data scientist +2 years of experience, i ALWAYS learn something new with your content! please nich, never stop doing this things, and also, never cut your smile in your face, even if your are having bugs!!

[pantherofficial]

I have been looking into gpu programming using numba and python for a while, this seems to be the best tutorial I was able to find so far.. . thank you

[todotasks]

Too hard to find high-quality content like this these days. ⚡

[erenbasak]

Hey this is super useful! I elected High Performance Computing and Microprocessors and Embedded Systems modules for my degree, and this channel has become my go-to guide.

[excolabirbuyuyecek]

LOL. Loved the graphic at 6:23! Brought tears to my eyes.

[RAVIShankar-bmou]

Thank you so much for this series! It's so clear and easy to follow

[basslvers]

wanted to comment that the information in this presentation is very well structured and the flow is excellent.

[adeeshaamabidu]

Love the channel Nicholas, have recently graduated from an NLP Master's degree and seeing you explain stuff in a simpler way and your coding challenges is really helping me connect with the material I've learned! Keep it up and I'll keep watching!

[youtubemullim]

I feel like Cuda has been demystified. Very glad I found your series.

[-oof]

Ahmad, thanks for taking time to create these videos. It is unfortunate that people view your videos and then feel inspired to complain about a free gift. Folks could just keep it moving or add helpful insights.

[muradhesenov]

the essence of Deep learning in a few lines of code... awesome

[rithusvlogtime]

Fantastic tutorials on CUDA. You deserve more followers.

[LouieVon]

This is the best introduction to CUDA I've seen, thanks a lot !

[tajaochrisciamae]

what a passionate tutorial! I wish you were my professor for my parallel programming course. Well done!