If you can parallelize your code by harnessing the power of the GPU, I bow to you. GPU code is usually abstracted away by by the popular deep learning frameworks, but knowing how it works is really useful. CUDA is the most popular of the GPU frameworks so we're going to add two arrays together, then optimize that process using it. I love CUDA!
Code for this video:
github.com/llS...
Alberto's Winning Code:
github.com/alb...
Hutauf's runner-up code:
github.com/hut...
Please Subscribe! And like. And comment. That's what keeps me going.
Follow me:
Twitter: / sirajraval
Facebook: / sirajology
More learning resources:
supercomputingb...
www.nvidia.com/...
devblogs.nvidi...
developer.nvid...
llpanorama.wor...
www.udacity.co...
lorenabarba.com...
cuda-programmin...
www.cc.gatech....
Join us in the Wizards Slack channel:
wizards.herokua...
No, Nvidia did not pay me to make this video lol. I just love CUDA.
And please support me on Patreon:
www.patreon.co...
Follow me:
Twitter: / sirajraval
Facebook: / sirajology Instagram: / sirajraval
Signup for my newsletter for exciting updates in the field of AI:
goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content! Join my AI community: chatgptschool.io/ Sign up for my AI Sports betting Bot, WagerGPT! (500 spots available):
www.wagergpt.co
5 окт 2024