CUDA Programming on Python
ฝัง
- เผยแพร่เมื่อ 11 พ.ค. 2024
- 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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12:36 This guy is a God !
very nice
So beautiful
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.
Thank you for the donation, it really means a lot !
@@AhmadBazzi No thank you !
Thank you so much for responding to my request for making a CUDA programming.
Wow amazing
Wow amazing
this was such an excellent video
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.
You just opened my eyes to parallel programming. Thanks for the quick overview.
Too hard to find high -quality content like this these days. Thank you so much
Too hard to find high-quality content like this these days. Thank you so much
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!
Excelent
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!
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
This was by far one of the most enlightening videos you have put up on your channel. Thanks and keep up the good work!!
Thank you so much for this series! It's so clear and easy to follow
This was a great video to me, I have very limited C++ experience and was looking for an explanation of CUDA. Another video like this could easily have been 70-80% over my head. This one was only about 15% whoosh. And now I actually find C++ interesting again!
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!!
wanted to comment that the information in this presentation is very well structured and the flow is excellent.
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.
what a passionate tutorial! I wish you were my professor for my parallel programming course. Well done!
and that's what I call a great tutorial. Thankyou sir. I wish you make more tutorials.
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 !
You saved me, i had to read the PointNet2 implementation for my BCS thesis. this made the job much easier!
Thanks for the video, I found the first half and the wrap up really excellent.
You are a lifesaver @Spencer, will do it next time i'm on the streaming rig!
Thank you for this great introduction to numba and more specifically numba+cuda.
Thank you so very much. This is the exact kind of material I was looking for on this very specific subject. Kudos.
OHHHH MANNN, I thought about doing that but I was debating whether I'd hit the 15 minute deadline already. Good suggestion @Julian!
Woah congrats @Ally 🎊 🎉 glad you’re enjoying the challenges, plenty more to come!!
holy shit, i was looking into this to speed up my mandelbrot-zooms and they are what you use as an example! This is a dream come true!
Excellent example of vector addition of using for loop and using CUDA
the essence of Deep learning in a few lines of code... awesome
Perfect Video! Saw was revealing to me to understand how it works. Thank you! I am a new subscriber of your channel. Regards from Buenos Aires, Argentina
Great video, I like this kind of video where you code some AI task counterclock, you teach us the concepts and show us the reality of implementing it👏
Thanks for making all these topics very approachable!
Oh Ahmad , your tutorials are incredible and inspiring....
Hey Ahmad , I love watching your videos because of the way you tell the story. Great graphics mate. Love the reference to rocket man too... lol keep up the good work.
Awesome video !! It's preety cool to see such theoretical concepts coded and explained like this. Keep going Nich !!
LOL. Loved the graphic at 6:23! Brought tears to my eyes.
Thank you very much for this tutorial. I would love to have the code available because typing it in myself from the video is a bit hard especially with the atocomplete on all the time. Keep up the good work.
This is the best introduction to CUDA I've seen, thanks a lot !
#
Ohh, yes, Thank you, and the documentation at nvidia site about CUDA is highly professionally written. Thank you.
Amazing! I'm learning so much watching you code. Thank you for sharing.
This was oddly intense. Great job Nicholas! Even though you ran out of time, this video is still a win to me. 😉
Thank you so much for this video. It has helped me massively to prepare for my computer science exam.
Ayyyy, so glad you like it @Patrick. For the last two weeks I've just been making videos on stuff I find hard or want to get my head around I figure it's not just me staring there at some of these concepts like huh?!? Thanks for checking it out!!
This is amazing! Thank you for taking effort to make it!
This is really helpful for my computing. Thank you.
I like how you did the website for documenting the video notes for reference later
Too hard to find high-quality content like this these days. ⚡
Wow It is really awesome! It is much better than a tutorial from university! Thanks!
I was needing this!!! Thanks a lot, Sir!!!!
I have no idea what kind of videos i am watching ... but i sure will learn
So stoked you liked it 🙏
Well just built a new rig with a 980ti and a 4790k so I'm gonna put that to test. Thank you for your wonderful explanation :D
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.
I feel like Cuda has been demystified. Very glad I found your series.
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This was really good. Thanks for posting this!
Love your videos. Please don't stop!
This is very helpful. Most people don't realize the overheads and code refactoring necessary to take advantages of the GPUs. I am going to refactor a simple MNIST training propgram I have which currently uses only Numpy. See if I can get meaningful improvements in training time.
Many thanks for the lucid explanation.
Great explanation! Fascinatingly clear
Hey, thanks for explanation! Very well done 👍 I am downloading CUDA 💪
Fantastic tutorials on CUDA. You deserve more followers.
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This reminds me a lot of the computer tutorial tapes from the 90s
This is excellent material! Thank you!
this was such an excellent video, thank you so much!
this is extremely helpful. you did an amazing job explaining the foundations
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PS. I really so moved for your stock price episode. thank you so sosososo much.
Excellent explanation, keep going with this content man ;)
Excellent, many thanks for your explanations
Awesome! learning never stops.
It's very informative and a good intro to CUDA programming. Thanks very much!
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Amazing, clear, and understandable!
YESSSS, right?! Glad you liked it Miguel!
opened my eyes to parallel programming
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The video was very helpful for me. Many thanks to the author for developing his audience with interesting and useful content
An insanely underrated series!!!
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really well done, very concise. thank you.
Absolutely lovely visuals!!!
Just what I needed! Thanks!
Thank you so much sir, you are an amazing human being !
Your videos are awesome , thanks a lot for this quality content :)
Very nice tutorial. I really liked it. It's brief, to the point and very clear. Thanks. Could you please make a video for the same example but in Linux?
It is effectively a very easy approach to harness the power of cuda in simple python scripts.
Sir,make more detailed sessions on CUDA,your explanation is great
Glad you liked it!
Once you initialized lr to 0.0, I knew you were going to forget to change it lol. Love the challenges tho, keep doing them, I think it would be cool to see how you implement a neural network from scratch
Thanks a million @Lakshman!! I try to keep it pretty tight so it’s a good challenge otherwise I know I’ll just talk for 22 minutes anyway😅
Great tutorial man!
Thanks for the video, subscribed! A suggestion : this small change to your code would demonstrate a real-world gradient descent solution for linear regression with noisy data. E.g. :
Very clear, I loved it !
Love your videos bro! Time to put down that redbull though lol just kidding happy holidays!
Really nice video, thank you for sharing!
such great work and thanks for your kind words! :)
Very well explained. The best CUDA explaination I have come across uptil now 😊😊. Keep up the spirits sir.👍👍
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Extremely good quality! wow
Really enjoyed this video!
wold love to see a video on what are a few CUDA programming challenges
Awesome tutorial, thanks.
Thanks a lot really got me started .
I'm doing an internship in a research lab and I'll have to program some kernels to implement Blas primitives, this video really helps :)
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Great talk, thank you ! Well structured and clear.
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thanks for the feedback!