CUDA (or Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for general purpose processing, an approach called general-purpose computing on GPUs (GPGPU). CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. CUDA is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC and OpenCL; and HIP by compiling such code to CUDA. CUDA was created by Nvidia. When it was first introduced, the name was an acronym for Compute Unified Device Architecture, but Nvidia later dropped the common use of the acronym. (Wikipedia).
A tour of CERN and its research facilities. Find out more about CERN: http://home.cern/ Produced by: CERN Video Productions Director: CERN Video Productions You can follow us on: cern.ch youtube.com/cerntv facebook.com/cern twitter.com/cern/ linkedin.com/company/cern instagram.com/cern
From playlist CERN - the Laboratory
月周回衛星「かぐや」のHDTVが観測した雨の海と虹の入り江 (C) JAXA/NHK
From playlist Earth's place in Solar System - Jaxa
ANCIENT CIVILIZATIONS : Inca and Mayan Empires
A look at the Ancient Civilizations of the Inca and Maya Empires. Discover the gems of the 15th Century Incan empire, a domain which covered much of South America. The splendid cities of Curzo and Chairana and the grandeur of Machu Picchu captivated the imagination and, unfortunately, gree
From playlist History and Biographies
AstroGPU Internals of the CUNBODY 1 Library - Tsuyoshi Hamada
Tsuyoshi Hamada Institute for Advanced Study November 9, 2007
From playlist Natural Sciences
World's Most Powerful Visible Diode Laser
"The NUBM44 Laser Diode" The World's Most Powerful
From playlist Lasers
This video briefly introduces CERN - what it is, a brief history, and what occurs there, and how it impacts' today's society. Like what I do? Support by buying me a coffee - www.buymeacoffee.com/physicshigh For on going support, support me at Patreon: www.patreon.com/physicshigh LIKE an
From playlist New here? A selection of what I do
William Horton: CUDA in Your Python: Effective Parallel Programming on the GPU
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.
From playlist PyColorado 2019
Data Science with Mathematica -- LibraryLink, CUDA, CUDALink, and CUDA through LibraryLink
In this video of the Data Science with Mathematica track I provide a very rudimentary introduction to LibraryLink, the CUDALink package, and the use of CUDA through LibraryLink, the latter being my preference, as it offers the greatest flexibility, and one can use all the features of your
From playlist Data Science with Mathematica
Applications of GPU Computation in Mathematica
With Mathematica, the enormous parallel processing power of Graphical Processing Units (GPUs) can be used from an integrated built-in interface. Incorporating GPU technology into Mathematica allows high-performance solutions to be developed in many areas such as financial simulation, image
From playlist Wolfram Technology Conference 2011
CUDA Explained - Why Deep Learning uses GPUs
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: https://youtu.be/DBVLcgq2Eg0?t=1340 🕒🦎 VIDEO SECTIONS 🦎🕒 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for l
From playlist PyTorch - Python Deep Learning Neural Network API
CUDA In Your Python: Effective Parallel Programming on the GPU
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.
From playlist Machine Learning
TensorFlow and Keras GPU Support - CUDA GPU Setup
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 t
From playlist TensorFlow - Python Deep Learning Neural Network API
So... This is another GPU stream that I should have been more prepared for. I'll contact a julia dev about the issues we had. Ultimately, I'm a big fan of JuliaGPU and want to work with it more! -- Watch live at https://www.twitch.tv/simuleios
From playlist Misc
月周回衛星「かぐや」のHDTVが観測したオリエンタレ・ベイスン (C)JAXA/NHK
From playlist Earth's place in Solar System - Jaxa
How to Implement Deep Learning Applications for NVIDIA GPUs with GPU Coder
GPU Coder™ generates readable and portable CUDA® code that leverages CUDA libraries like cuBLAS and cuDNN from the MATLAB® algorithm, which is then cross-compiled and deployed to NVIDIA® GPUs from the Tesla® to the embedded Jetson™ platform. Learn more about GPU Coder: https://goo.gl/iur97
From playlist Protoype, Verify, and Deploy to GPUs
How To Install CUDA, cuDNN, Ubuntu, Miniconda | ML Software Stack | Part 3/3
❤️ Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany 👨👩👧👦 Join our Discord community 👨👩👧👦 https://discord.gg/peBrCpheKE In this video we go through the process of installing: * Ubuntu OS * Miniconda * CUDA toolkit and NVIDIA drivers * cuDNN * running benchmark
From playlist Miscellaneous
Offloading & CUDA: Parallelism in C++ #3/3 (also OpenMP, OpenACC, GPU & Coprocessors like Xeon Phi)
Computer programs can be made faster by making them do many things simultaneously. Let’s study three categorical ways to accomplish that in GCC. In the third episode, we study ways to offload code to various accelerators such as GPU on a graphics card. We also explore CUDA. The previous e
From playlist Programming
A.I. Just Designed An Enzyme That Eats Plastic
» Podcast I Co-host: https://www.youtube.com/channel/UC6jKUaNXSnuW52CxexLcOJg » ColdFusion Discord: https://discord.gg/coldfusion » Twitter | @ColdFusion_TV » Instagram | coldfusiontv --- About ColdFusion --- ColdFusion is an Australian based online media company independently run by Dag
From playlist Technology