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Explore CUDA Simply Explained - GPU vs CPU Parallel Computing for Beginners with 330,740 views, 9,612 likes, and 517 comments. Experience the impact of this video content that has captured audience attention.

In this tutorial, we will talk about CUDA and how it helps us accelerate the speed of our programs. Additionally, we will discuss the difference between processors (CPUs) and graphic cards (GPUs) and how come we can use both to process code. By the end of this video - we will install CUDA and perform a quick speed test comparing the speed of our GPU with the speed of our CPU. We will create 2 extremely large data structures with PyTorch and we will multiply one by the other to test the performance. Specifically, I'll be comparing Nvidia's GeForce RTX 3090 GPU with Intel's i9-12900K 12th-Gen Alder Lake Processor (with DDR5 memory). I'll be posting some more advanced benchmarks in the next few tutorials, as the code I'm demonstrating in this video is 100% beginner-friendly! ⏲️ Time Stamps ⏲️ ***************************************** 00:00 - what is CUDA? 00:47 - how processors (CPU) operate? 01:42 - CPU multitasking 03:16 - how graphic cards (GPU) operate? 04:02 - how come GPUs can run code faster than CPUs? 04:59 - benefits of using CUDA 06:03 - verify our GPU is capable of CUDA 06:48 - install CUDA with Anaconda and PyTorch 09:22 - verify if CUDA installation was successful 10:32 - CPU vs GPU speed test with PyTorch 14:20 - freeze CPU with torch.cuda.synchronize() 15:51 - speed test results 17:55 - CUDA for systems with multiple GPUs 18:28 - next tutorials and thanks for watching! 🔗 Important Links 🔗 ***************************************** ⭐ My Anaconda Tutorial for Beginners: https://youtu.be/MUZtVEDKXsk ⭐ My CUDA vs. TensorRT Tutorial for Beginners: https://youtu.be/iFADsRDJhDM ⭐ CUDA Enabled GPUS: https://developer.nvidia.com/cuda-gpus ⭐ Complete Notebook Code: https://github.com/MariyaSha/CUDA_speedtest 💻 Install with VENV instead of Anaconda (LINUX) 💻 ***************************************** ❗install venv: $ sudo apt-get install -y python3-venv 🥇create working environment: $ python3 -m venv my_env 🥈activate working environment: $ source my_env/bin/activate 🥉install PIP3 and PyTorch+CUDA: (my_env) $ sudo apt install python3-pip (my_env) $ pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html 🏆more information about VENV: https://docs.python.org/3/library/venv.html 🏆more information about installing Pytorch: https://pytorch.org/get-started/locally/ 🙏SPECIAL THANK YOU 🙏 ***************************************** Thank you so much to Robert from Nvidia for helping me with the speed test code! Thank you to SFX Buzz for the scratched record sound: https://www.sfxbuzz.com/ Thank you to Flat Icon for the beautiful icon graphics: https://www.flaticon.com/

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