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Intro to CUDA - An introduction, how-to, to NVIDIA's GPU parallel programming architecture

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Introduction to NVIDIA's CUDA parallel architecture and programming model. Learn more by following 🤍gpucomputing on twitter.

CUDA Simply Explained - GPU vs CPU Parallel Computing for Beginners

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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: 🤍 ⭐ My CUDA vs. TensorRT Tutorial for Beginners: 🤍 ⭐ CUDA Enabled GPUS: 🤍 ⭐ Complete Notebook Code: 🤍 💻 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 torch1.10.1+cu113 torchvision0.11.2+cu113 torchaudio0.10.1+cu113 -f 🤍 🏆more information about VENV: 🤍 🏆more information about installing Pytorch: 🤍 🙏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: 🤍 Thank you to Flat Icon for the beautiful icon graphics: 🤍

An Introduction to GPU Programming with CUDA

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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: 🤍 Alberto's Winning Code: 🤍 Hutauf's runner-up code: 🤍 Please Subscribe! And like. And comment. That's what keeps me going. Follow me: Twitter: 🤍 Facebook: 🤍 More learning resources: 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 Join us in the Wizards Slack channel: 🤍 No, Nvidia did not pay me to make this video lol. I just love CUDA. And please support me on Patreon: 🤍 Follow me: Twitter: 🤍 Facebook: 🤍 Instagram: 🤍 Signup for my newsletter for exciting updates in the field of AI: 🤍 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Intro to CUDA (part 1): High Level Concepts

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CUDA Teaching Center Oklahoma State University ECEN 4773/5793

Getting Started with NVIDIA GPU CUDA Core Programming Using Visual Studio in 2021

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In this video, we talk about how why GPU's are better suited for parallelized tasks. We go into how a GPU is better than a CPU at certain tasks. Finally, we setup the NVIDIA CUDA programming packages to use the CUDA API in Visual Studio. GPUs are a great platform to executed code that can take advantage of hyper parallelization. For example, in this video we show the difference between adding vectors on a CPU versus adding vectors on a GPU. By taking advantage of the CUDA parallelization framework, we can do mass addition in parallel. Join me on Discord!: 🤍 Support me on Patreon!: 🤍

What Are NVIDIA CUDA Cores And What Do They Mean For Gaming? [Simple]

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✅ Read full article ➡️ 🤍 ⭐️ Subscribe ➡️ 🤍 Best Graphics Cards ➡️ 🤍 What are NVIDIA Cuda Cores and what do they mean for gaming? Should you keep them in mind when choosing a new GPU? What's AMD's counterpart if there is one? Here's everything you should know about NVIDIA Cuda Cores! Keep watching. Timestamps: 0:00 Intro 1:11 What are CUDA Cores 2:02 Benefits of CUDA Cores in Gaming 2:58 How Many CUDA Cores Do You Need? 4:06 CUDA Cores vs Stream Processors 4:48 Conclusion

NVIDIA CUDA for premiere pro

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In this short and precise tutorial, I have shown how to activate NVIDIA CUDA for Adobe Premiere Pro. NVIDIA Driver Download: 🤍 GPU-Z Download: 🤍 Background Music: 🤍 If you found this tutorial helpful, please show your support by liking, commenting, and sharing this video. Also, click on the bell icon to get the notification for future uploads. Thanks. Facebook: 🤍 Youtube: 🤍

Installing CUDA Toolkit on Windows

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See how to install the CUDA Toolkit followed by a quick tutorial on how to compile and run an example on your GPU. Learn more at the blog: 🤍

What Are CUDA Cores?

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- If you've ever owned an Nvidia graphics card, chances are that card featured CUDA technology, a parallel-processing GPU format suitable for developers and APIs alike. What makes it special? Let's dive deeper. FACEBOOK: 🤍 TWITTER: 🤍 INSTAGRAM: 🤍 Subscribe to our channel! Thanks for learning with us! MUSIC: 'One' by The Last 'Blue Flame' by Mich DISCLOSURES: All Genius links are tied to our Amazon Associate account, from which we earn a small sales commission. Links containing a 'bit.ly' reference forwarding to Newegg are tied to our CJ account, from which we earn a small sales commission. All sponsored links and comments will contain the word "SPONSOR" or "AD." Any additional revenue stream will be disclosed with similar verbiage.

Your First CUDA C Program

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Learn how to write, compile, and run a simple C program on your GPU using Microsoft Visual Studio with the Nsight plug-in. Find code used in the video at: 🤍 Learn more at the blog: 🤍

George Hotz | Programming | aside: nvidia open source kernel driver | geohot/cuda_ioctl_sniffer

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Date of stream 21 May 2022. Live-stream chat added as Subtitles/CC - English (Twitch Chat). Stream title: aside: nvidia open source kernel driver Source files: - 🤍 - 🤍 - 🤍 - 🤍 - 🤍 - 🤍 Follow for notifications: - 🤍 Support George: - 🤍 Programming playlist: - 🤍 George Hotz Gear: Gaming computer - AMD Ryzen 9 5950X - 64GB RAM - Nvidia GeForce RTX 3080 Ti Streaming computer - Apple 16-inch MacBook Pro - LG UltraFine 5K Display - Blue Yeti microphone - Apple Magic Keyboard - HHKB White - tmux & Vim & Visual Studio Code with Vim Key Bindings and other 🤍 Chapters: 00:00:00 stream intro 00:03:00 github.com/geohot/tinyvoice conclusion 00:10:08 gaming computer 00:16:17 reading github.com/NVIDIA/open-gpu-kernel-modules 00:54:23 comment about nvidia open sourcing 00:55:07 google CDP score trigger 00:57:15 the future 00:58:37 don't try to fight them and don't be a republican 01:00:00 continuing reading code 01:05:02 o'reilly book ad popup trigger 01:06:30 goal for the stream 01:08:45 nvidia 4090 spec comment 01:11:46 why really triggered by CDP score 01:16:30 Martin Shkreli stream comment 01:17:30 continuing working on code 01:49:40 found all the megabytes VID_HEAP_CONTROL 01:58:47 figure out what different flags mean 02:22:38 debug level 02:37:06 we made tinyvoice work now look into nvidia opensource drivers 03:14:20 pause stream for food 03:15:33 what we are doing on stream 03:16:01 inception George watching himself explaining what he said before 03:16:53 we are trying to get to the bottom of what cuda is actually doing 03:18:42 hacker/hater news commenters rant 03:23:11 checking youtube comments 03:23:46 always remember about commenters on the internet 03:25:25 making food 03:34:24 continuing working on code 04:03:34 _cudart656 search 0 results and shady duckduckgo search result link 04:04:58 why open sourcing nvidia will win and hacking group Lapsus$ comment 04:07:04 trying to install qira 04:08:27 where are the exploits 04:08:51 Nvidia Jetson AGX Xavier and mobile division comment 04:10:20 continuing working on code 04:36:40 comment about NXP S32G274 processor 04:41:18 shill for comma jobs 04:43:50 j_blow raid 04:44:27 George explains what his is doing in the stream once again 05:11:40 George being nice to new people 05:12:05 George taking a minute to explain things to noobs 05:15:03 why George can't disable sub chat 05:16:50 George continue to explain things to noobs (kernel talk, ...) 05:19:30 how the sniffer works 05:25:29 lunixbochs get's VIP of George's stream for all good suggestions 05:26:49 continuing working on code 06:35:20 we should read nouveau source code 06:41:08 we will launch a cuda kernel without the nvidia qlaunch kernels We are not affiliated with comma.ai. Official communication channels: - 🤍 - 🤍 - 🤍 - 🤍 - 🤍 - 🤍 How to get a job: - 🤍 How to collaborate: - 🤍 Buy things to support comma.ai: - 🤍 Are you interested in openpilot? Knowledge base: - 🤍 Check out the code: - 🤍 Is my car supported? - 🤍 Frequently Asked Questions: - 🤍 How to setup openpilot: - 🤍 Comma Secure Shell: - 🤍 API Documentation: - 🤍 CAN analysis tool: - 🤍 Review and annotate your driving data: - 🤍 Leaderboard: - 🤍 Comma Connect App: - 🤍 - 🤍 Research: - 🤍 - 🤍 - 🤍 Official George Hotz communication channels: - 🤍 - 🤍 - 🤍 - 🤍 - 🤍 - 🤍 We archive George Hotz and comma.ai videos for fun. Follow for notifications: - 🤍 Unofficial communities and resources: - 🤍 - 🤍 - 🤍 Thank you for reading and using the SHOW MORE button. We hope you enjoy watching George's videos as much as we do. See you at the next video.

CUDA Explained - Why Deep Learning uses GPUs

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Artificial intelligence with PyTorch and CUDA. Let's discuss how CUDA fits in with PyTorch, and more importantly, why we use GPUs in neural network programming. Strange Loop: 🤍 🕒🦎 VIDEO SECTIONS 🦎🕒 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:30 Help deeplizard add video timestamps - See example in the description 13:03 Collective Intelligence and the DEEPLIZARD HIVEMIND 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👋 Hey, we're Chris and Mandy, the creators of deeplizard! 👀 CHECK OUT OUR VLOG: 🔗 🤍 👉 Check out the blog post and other resources for this video: 🔗 🤍 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 🤍 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 🤍 🤜 Support collective intelligence, create a quiz question for this video: 🔗 🤍 🚀 Boost collective intelligence by sharing this video on social media! ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Tammy Prash Zach Wimpee 👀 Follow deeplizard: Our vlog: 🤍 Facebook: 🤍 Instagram: 🤍 Twitter: 🤍 Patreon: 🤍 YouTube: 🤍 🎓 Deep Learning with deeplizard: Fundamental Concepts - 🤍 Beginner Code - 🤍 Intermediate Code - 🤍 Advanced Deep RL - 🤍 🎓 Other Courses: Data Science - 🤍 Trading - 🤍 🛒 Check out products deeplizard recommends on Amazon: 🔗 🤍 📕 Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard's link: 🔗 🤍 🎵 deeplizard uses music by Kevin MacLeod 🔗 🤍 🔗 🤍 ❤️ Please use the knowledge gained from deeplizard content for good, not evil.

Why CUDA "Cores" Aren't Actually Cores, ft. David Kanter

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We talk about NVIDIA CUDA Cores vs. AMD Stream Processors and why neither is actually a "core," featuring David Kanter of Real World Tech. Ad: Buy the Corsair Dark Core SE on Amazon (🤍 Check out Real World Tech here: 🤍 Read David's article on TBR: 🤍 David Kanter of Real World Tech (formerly Microprocessor Report) joins us to discuss why CUDA "Cores" aren't actually cores, later explaining the differences between CPU & GPU cores, stream processors vs. CUDA cores, and more. The discussion follows GPU architecture and explains the building blocks of a GPU, with details on streaming multiprocessors (SMs), multiply-add operations, texture map units (TMUs), and so forth. This is a great opportunity to learn from an expert about GPU architecture basics, and to help demystify some of the marketing language used in the industry. We have a new GN store: 🤍 Like our content? Please consider becoming our Patron to support us: 🤍 Please like, comment, and subscribe for more! Follow us in these locations for more gaming and hardware updates: t: 🤍 f: 🤍 w: 🤍 Host: Steve Burke Expert: David Kanter Video: Andrew Coleman Links to Amazon and Newegg are typically monetized on our channel (affiliate links) and may return a commission of sales to us from the retailer. This is unrelated to the product manufacturer. Any advertisements or sponsorships are disclosed within the video ("this video is brought to you by") and above the fold in the description. We do not ever produce paid content or "sponsored content" (meaning that the content is our idea and is not funded externally aside from whatever ad placement is in the beginning) and we do not ever charge manufacturers for coverage.

CUDA Part A: GPU Architecture Overview and CUDA Basics; Peter Messmer (NVIDIA)

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Programming for GPUs Course: Introduction to OpenACC 2.0 & CUDA 5.5 - December 4-6, 2013

Latest Updates to CUDA Developer Tools

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The latest updates to NVIDIA Developer Tools help users debug, profile, and optimize CUDA applications. This video is a brief overview of several of these new features. This includes network profiling and multi-report tiling in Nsight Systems and the occupancy calculator and register dependency tracking in Nsight Compute. There are also several new features for OptiX developers like improved profiling with Nsight Compute, an acceleration structure viewer, and new support in Compute Sanitizer. The features introduced in this video help CUDA developers understand their applications and create the best possible versions. For more information see the Developer Tools homepage. 🤍 #GTC22, #GTC, #NVIDIA, #NVIDIADev, #DevTools, #CUDA, #Nsight, #OptiX, #developer

Installing Latest TensorFlow version with CUDA, cudNN and GPU support - Step by step tutorial 2021

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In this video I show you the freakishly difficult task of setting up and installing the latest tensorflow version with GPU support on Windows 10 :) GO HERE FIRST: 🤍 1. Microsoft Visual Studio * 🤍 2. the NVIDIA CUDA Toolkit * 🤍 3. NVIDIA cuDNN * 🤍 4. Python (check compatible version from first link) conda create name tf_2.4 python3.8 5. Tensorflow (with GPU support) pip install tensorflow GitHub Repository: 🤍 ✅ Equipment I use and recommend: 🤍 ❤️ Become a Channel Member: 🤍 ✅ One-Time Donations: Paypal: 🤍 Ethereum: 0xc84008f43d2E0bC01d925CC35915CdE92c2e99dc ▶️ You Can Connect with me on: Twitter - 🤍 LinkedIn - 🤍 GitHub - 🤍 TensorFlow Playlist: 🤍

Cuda Graphs Explained | Nvidia Cuda | Cuda Education

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Cuda Graphs Tutorial: 🤍 DISCLAIMER: Use at your own risk! This code and/or instructions are for teaching purposes only. CUDA Education does not guarantee the accuracy of this code in any way. The code and instructions on this site may cause hardware damage and/or instability in your system. This code and/or instructions should not be used in a production or commercial environment. Any liabilities or loss resulting from the use of this code and/or instructions, in whole or in part, will not be the responsibility of CUDA Education. All rights reserved. This code is the property of CUDA Education. Please contact CUDA Education at cudaeducation🤍gmail.com if you would like to use this code in any way, shape or form.

Faster Rendering Using the Nvidia Cuda Toolkit

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Big Thanks goes to Barnaclues ; 🤍 Nvidia Cuda - 🤍 Once youve downloaded the toolkit and installed. Go to: Computer: Program Files - Select Program Files {x86) if running 32bit version Select the Adobe Folder (app you use for rendering) Check you have the file called cuda_supported_cards ( DO NOT OPEN IT ) Open Note pad ( AS ADMINISTRATOR) Now open the cuda_supported_cards from the file open menu in NOTE PAD. If your graphics card isnt shown. Add it in the same format as shown in the file. Once down- SAVE NEXT Open the Nvidia Control Panel and select; MANAGE 3D SETTINGS Select the second tab along in the right hand side window. Add the file you want i.e Adobe Premier if not in the drop down menu then add by selecting add then selecting the .exe file from within the program files folder. Check to see that teh file now states CUDA GPUs - Use global setting (All) If so all done. If you havve the program open then close and re-open for the setting to take effect. Thanks for watching MY SETUP Case : Green Neptune Tower Case Power Supply : Aerocool VP Pro 850 Watt Branded PSU Motherboard : Gigabyte H81M-S2PV CPU : Intel I7 4th Gen 4770 Quad Core 3.4Ghz (turbo 3.9Ghz) CPU Hard Drive : 1tb Sata Hard Drive Memory : 16gb DDR3 1600mhz Corsair Vengeance Memory Graphics Card : Nvidia GTX 770 2gb (Tripple screen support via DVI / HDMI / DISPLAY PORT) Optical Drive : 24x Dual Layer DVD Writer Connections : 6 x USB 2.0 / 2 x USB 3.0 / LAN / Sound 3 x BenQ GL2450HM 24 inch Widescreen LED Multimedia Monitor - Glossy Black (1920x1080, 2ms, VGA, DVI-D, HDMI, Windows 7 Compatible) Blue Yeti USB Mic Elgato HD Game Capture

Learning CUDA 10 Programming : The NVIDIA Visual Profiler | packtpub.com

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This video tutorial has been taken from Learning CUDA 10 Programming. You can learn more and buy the full video course here 🤍 Find us on Facebook 🤍 Follow us on Twitter - 🤍

NVIDIA CUDA - Introduction to CUDA5 by Ian Buck

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Ian Buck provides a brief overview of the key new technologies introduced with CUDA 5. More information at 🤍

Nvidia CUDA С Уроки. Начало. Введение. Параллельное программирование GPU.

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Nvidia CUDA С Уроки. Начало. Введение. Параллельное программирование GPU. 🤍 Стать спонсором канала 🤍 Яндекс кошелек - 4100 1163 2706 8392 🤍 🤍 список видеороликов (🤍

Creating a CUDA GPU Computing Environment

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Steps for Windows 10/Cuda 8.0 Pre-requisites: * You must have compute compatible Nvidia GPU hardware * Make sure you have Visual Studio 2015 or 2013 installed first * Download and Install cuda_8.0.44_win10.exe (🤍 * Install DXSDK (🤍 Check your environment variables for the following CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0 CUDA_PATH_V8_0=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0 DXSDK_DIR=C:\Program Files (x86)\Microsoft DirectX SDK (June 2010)\ NVCUDASAMPLES8_0_ROOT=C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0 NVCUDASAMPLES_ROOT=C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0 NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\ Check PATH contains C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\libnvvp Launch the "Samples_vs2015" (or vs2013 depending) solution file located in C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0

Tutorial 33- Installing Cuda Toolkit And cuDNN For Deep Learning

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Cuda Toolkit: 🤍 cuDnn: 🤍 Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more 🤍 Please do subscribe my other channel too 🤍 Connect with me here: Twitter: 🤍 Facebook: 🤍 instagram: 🤍

Launching computations using an Nvidia GPU w/ CUDA in C

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Explore some massive parallel computing courteous of our friend the Graphics Processing Unit. Hope you enjoyed the video! CUDA Toolkit Download: 🤍 Check out this code here: 🤍 Join my Discord server to chat with me: 🤍 Check out some code on my GitHub: 🤍 Tweet me something funny on Twitter: 🤍 Say hi over at Facebook: 🤍 Sincerely, Engineer Man

Introduction to GPU Programming with CUDA and Thrust

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High-performance computing is now dominated by general-purpose graphics processing unit (GPGPU) oriented computations. How can we leverage our knowledge of C to program the GPU? NVIDIA's answer to general-purpose computing on the GPU is CUDA. CUDA programs are essentially C programs, but have some differences. CUDA comes as a Toolkit SDK containing a number of libraries that exploit the resources of the GPU: fast Fourier transforms, machine learning training and inference, etc. Thrust is a C template library for CUDA. In this month's meeting, Richard Thomson will present a brief introduction to CUDA with the Thrust library to program the GPU. Programming the GPU with CUDA is a huge topic covered by lots of libraries, tutorials, videos, and so-on, so we will only be able to present an introduction to the topic. You are encouraged to explore more on your own! Utah C Programmers meetup: 🤍 Utah C Programmers blog: 🤍 CUDA: 🤍 Thrust: 🤍

Explicación Nvidia Cuda.¿ Qué es ? ¿ Para que sirve ? *Aclara tus dudas en 5 min.* 1080p/60Fps

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Si tienes cualquier duda déjala en los comentarios y te ayudaré. Gracias por ver mis videos.

Introduction to GPU Programming with CUDA and Thrust

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High-performance computing is now dominated by general-purpose graphics processing unit (GPGPU) oriented computations. How can we leverage our knowledge of C to program the GPU? NVIDIA's answer to general-purpose computing on the GPU is CUDA. CUDA programs are essentially C programs, but have some differences. CUDA comes as a Toolkit SDK containing a number of libraries that exploit the resources of the GPU: fast Fourier transforms, machine learning training and inference, etc. Thrust is a C template library for CUDA. In this month's meeting, Richard Thomson will present a brief introduction to CUDA with the Thrust library to program the GPU. Programming the GPU with CUDA is a huge topic covered by lots of libraries, tutorials, videos, and so-on, so we will only be able to present an introduction to the topic. You are encouraged to explore more on your own! PUBLICATION PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). Link: 🤍

NVIDIA Cuda 10 Simulation Samples

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In this demo, we review NVIDIA CUDA 10 Toolkit Simulation Samples. Compiled in C and run on GTX 1080. * fluidsGL * nbody * oceanFFT * particles * smokeParticles More information at: 🤍 🤍

Setting Up CUDA, CUDNN, Keras, and TensorFlow on Windows 11 for GPU Deep Learning

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Complete walkthrough of installing TensorFlow/Keras with GPU support on Windows 11. We make use of a "pip install" rather than conda, to ensure that we get the latest version of TensorFlow. This requires installing Visual C, CUDA, CuDNN, as well as the Python libraries. Guide: 🤍 python -m ipykernel install user name tensorflow display-name "Python 3.9 (tensorflow)" 0:54 Installation Guides 2:03 Step 1: NVIDIA Video Driver 3:49 Step 2: Visual C 7:04 Step 3: CUDA 8:20 Step 4: CuDNN 12:38 Step 5: Anaconda and Miniconda 15:21 Step 6: Jupyter 16:31 Step 7: Environment 17:16 Step 8: Jupyter Kernel 18:13 Step 9: TensorFlow/Keras 19:46 Problems? 21:18 Test Jupyter

Introduction to GPU Computing with MATLAB

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Speed up your MATLAB® applications using NVIDIA® GPUs without needing any CUDA® programming experience. Parallel Computing Toolbox™ supports more than 700 functions that let you use GPU computing. Any GPU-supported function automatically runs using your GPU if you provide inputs as GPU arrays, making it easy to convert and evaluate GPU compute performance for your application. In this video, watch a brief overview, including code examples and benchmarks. In addition, discover options for getting access to a GPU if you do not have one in your desktop computing environment. Also, learn about deploying GPU-enabled applications directly as CUDA code generated by GPU Coder™. Parallel Computing Toolbox: 🤍 Get a free product trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See what's new in MATLAB and Simulink: 🤍 © 2022 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

Nvidia CUDA in Aktion

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Nvidia zeigt auf der Cebit 2009 CUDA in Aktion

Nvidia CUDA. Эволюция GPU. Краткий экскурс.

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23.10.2018

Nvidia CUDA. Эволюция GPU. Краткий экскурс. 🤍 Стать спонсором канала 🤍 Яндекс кошелек - 4100 1163 2706 8392 🤍 🤍 список видеороликов (🤍

CUDACast #10a - Your First CUDA Python Program

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In this CUDACast video, we'll see how to write and run your first CUDA Python program using the Numba Compiler from Continuum Analytics.

CUDA CORE & STREAM PROCESSORS | Cosa sono e a cosa servono

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03.07.2017

Cosa sono i Cuda Core di Nvidia e gli Stream Processors di AMD? A cosa servono e come funzionano? Scopriamolo. Schede grafiche consigliate: NVIDIA (🤍 e AMD (🤍 FACEBOOK ► 🤍 INSTAGRAM ► 🤍 TWITTER ► 🤍 ISCRIVITI ►🤍 FACEBOOK PERSONALE ► 🤍

TensorFlow and Keras GPU Support - CUDA GPU Setup

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00:15:54
21.05.2020

In this episode, we'll discuss GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU! 🕒🦎 VIDEO SECTIONS 🦎🕒 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:30 Help deeplizard add video timestamps - See example in the description 15:24 Collective Intelligence and the DEEPLIZARD HIVEMIND 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👋 Hey, we're Chris and Mandy, the creators of deeplizard! 👀 CHECK OUT OUR VLOG: 🔗 🤍 👉 Check out the blog post and other resources for this video: 🔗 🤍 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 🤍 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 🤍 🤜 Support collective intelligence, create a quiz question for this video: 🔗 🤍 🚀 Boost collective intelligence by sharing this video on social media! ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Tammy Prash Zach Wimpee 👀 Follow deeplizard: Our vlog: 🤍 Facebook: 🤍 Instagram: 🤍 Twitter: 🤍 Patreon: 🤍 YouTube: 🤍 🎓 Deep Learning with deeplizard: Fundamental Concepts - 🤍 Beginner Code - 🤍 Intermediate Code - 🤍 Advanced Deep RL - 🤍 🎓 Other Courses: Data Science - 🤍 Trading - 🤍 🛒 Check out products deeplizard recommends on Amazon: 🔗 🤍 📕 Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard's link: 🔗 🤍 🎵 deeplizard uses music by Kevin MacLeod 🔗 🤍 🔗 🤍 ❤️ Please use the knowledge gained from deeplizard content for good, not evil.

NVIDIA CUDA Tutorial 5: Memory Overview

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00:21:27
09.07.2013

The GPU has a complicated but flexible set of different memories. It's sometimes called a memory hierarchy. Each type of memory has various characteristics which we'll look into individually later. This tutorial is a short introduction to all the memories. These memories can be confusing. In many instances, they are the same stuff, for instance, the L1 cache and shared memory are the same bytes. The texture, global, constant and local memory are the same bytes as well. At the end I've put a small demonstration of the NVidia Visual Profiler, a program which I think everybody that codes CUDA should definitely have a look at. The website with the Device Query program is: 🤍

CUDA In Your Python: Effective Parallel Programming on the GPU

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00:29:28
24.06.2019

It’s 2019, and Moore’s Law is dead. CPU performance is plateauing, but GPUs provide a chance for continued hardware performance gains, if you can structure your programs to make good use of them. In this talk you will learn how to speed up your Python programs using Nvidia’s CUDA platform. EVENT: PyTexas2019 SPEAKER: William Horton PUBLICATION PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). ATTRIBUTION CREDITS: Original video source: 🤍

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