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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!: 🤍
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!
In this video, presenter Noel Chalmers introduces GPU programing concepts specific to ROCm. CTA: 🤍 CTA: 🤍 Watch the next video in the series: 🤍 View the full playlist: 🤍 * Subscribe: 🤍 Like us on Facebook: 🤍 Follow us on Twitter: 🤍 Follow us on Twitch: 🤍 Follow us on Linkedin: 🤍 Follow us on Instagram: 🤍 ©2020 Advanced Micro Devices, Inc. AMD, the AMD Arrow Logo, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other names are for informational purposes only and may be trademarks of their respective owners.
In this tutorial I will go over the following: When to use the GPU for calculations over the CPU How to write Metal Compute Kernel Functions How multithreading with the GPU works How Threadgroups work The difference in time taken for the GPU vs the CPU Why the GPU shouldn't be called the GPU. Episode Source Code: 🤍 Resources: Threads and Threadgroups: - 🤍 - 🤍 - 🤍 Metal Shading Language Specification: 🤍 Become A Patron: 🤍 Discord: Join me on Discord for discussions about Metal. I am always open to talk code :) 🤍 Affiliate Links: Sweet Standing Desks: 🤍 Blender Tutorials: 🤍
#GPU #C #AccuConf Parallel programming can be used to take advance of multi-core and heterogeneous architectures and can significantly increase the performance of software. It has gained a reputation for being difficult, but is it really? Modern C has gone a long way to making parallel programming easier and more accessible; providing both high-level and low-level abstractions. C11 introduced the C memory model and standard threading library which includes threads, futures, promises, mutexes, atomics and more. C17 takes this further by providing high level parallel algorithms; parallel implementations of many standard algorithms; and much more is expected in C20. The introduction of the parallel algorithms also opens C to supporting non-CPU architectures, such as GPU, FPGAs, APUs and other accelerators. This talk will show you the fundamentals of parallelism; how to recognise when to use parallelism, how to make the best choices and common parallel patterns such as reduce, map and scan which can be used over and again. It will show you how to make use of the C standard threading library, but it will take this further by teaching you how to extend parallelism to heterogeneous devices, using the SYCL programming model to implement these patterns on a GPU using standard C. - Michael Wong is the Vice President of Research and Development at Codeplay Software, a Scottish company that produces compilers, debuggers, runtimes, testing systems, and other specialized tools to aid software development for heterogeneous systems, accelerators and special purpose processor architectures, including GPUs and DSPs. He is now a member of the open consortium group known as Khronos and is Chair of the C Heterogeneous Programming language SYCL, used for GPU dispatch in native modern C (14/17), OpenCL, as well as guiding the research and development teams of ComputeSuite, ComputeAorta/ComputeCPP. For twenty years, he was the Senior Technical Strategy Architect for IBM compilers. He is a member of the ISO C Directions Group (DG), and the Canadian Head of Delegation to the ISO C Standard and a past CEO of OpenMP. He is also a Director and VP of ISOCPP.org, and Chair of all Programming Languages for Canada’s Standard Council. He has so many titles, it’s a wonder he can get anything done. He chairs WG21 SG14 Games Development/Low Latency/Financial/Embedded Devices and WG21 SG5 Transactional Memory, and is the co-author of a book on C and a number of C/OpenMP/Transactional Memory features including generalized attributes, user-defined literals, inheriting constructors, weakly ordered memory models, and explicit conversion operators. Having been the past C team lead to IBM’s XL C compiler means he has been messing around with designing the C language and C compilers for twenty-five years. His current research interest, i.e. what he would like to do if he had time is in the area of parallel programming, future programming models for Neural network, AI, Machine vision, safety/critical/ programming vulnerabilities, self-driving cars and low-power devices, lock-free programming, transactional memory, C benchmark performance, object model, generic programming and template metaprogramming. He holds a B.Sc from University of Toronto, and a Masters in Mathematics from University of Waterloo. He has been asked to speak/keynote at many conferences, companies, research centers, universities, including CPPCON, Bloomberg, U of Houston, U of Toronto, ACCU, CNow, Meeting C, ADC, CASCON, Bloomberg, CERN, Barcelona Supercomputing Center, FAU Erlangen, LSU, Universidad Carlos III de Madrid, Texas A&M University, Parallel, KIT School, CGO, IWOMP/IWOCL, Code::dive, many C Users group meetings, Euro TM Graduate School, and Going Native. He is the current Editor for the Concurrency TS and the Transactional Memory TS. 🤍 - Future Conferences: ACCU 2019 Autumn Conference, Belfast (UK): 2019-11-11 and 2019-11-12. ACCU 2020 Spring Conference, Bristol (UK), Marriott City Centre: 2020-03-24 to 2020-03-28. - ACCU Website: 🤍accu.org ACCU Conference Website: conference.accu.org ACCU Twitter: 🤍ACCUConf ACCU YouTube: 🤍 Filmed and Edited by Digital Medium Ltd - events.digital-medium.co.uk Contact: events🤍digital-medium.co.uk
Julia has several packages for programming GPUs, each of which support various programming models. In this workshop, we will demonstrate the use of three major GPU programming packages: CUDA.jl for NVIDIA GPUs, AMDGPU.jl for AMD GPUs, and oneAPI.jl for Intel GPUs. We will explain the various approaches for programming GPUs with these packages, ranging from generic array operations that focus on ease-of-use, to hardware-specific kernels for when performance matters. Most of the workshop will be vendor-neutral, and the content will be available for all supported GPU back-ends. There will also be a part on vendor-specific tools and APIs. Attendees will be able to follow along, but are recommended to have access to a suitable GPU for doing so. Materials 🤍 🤍 Enjoyed the workshop? Consider sponsoring us on GitHub: 🤍 00:00 Welcome! 00:24 Welcome 01:20 Outline 02:44 JuliaGPU packages 04:08 JuliaGPU back-ends 05:34 GPU Architecture 07:25 Parallel programming models 08:55 Follow along and links to notebooks, JuliaHub 12:37 Start of tuturial with notebook 16:00 Array programming 28:20 Kernel programming 34:32 Parallel programming + questions 58:40 Profiling 1:01:50 Profiling: NVIDIA Nsight Systems: live example 1:11:00 Profiling: NVIDIA Nsight Compute: live example → optimize single kernel invocation 1:19:05 Common issues: unsupported array operations 1:21:50 Common issues: unsuppored kernel operations 1:27:40 Parallel programming issues 1:31:55 Tour of accompanying Github repo 1:32:40 Case Study I: Image processing using AMDGPU 1:57:00 Break 2:01:30 Case Study II: Fun with arrays, Machine Learning 2:10:47 Case Study III: Random number generators 2:22:10 Kernel abstractions 2:42:10 Example: Solving heat equation with GPU 2:56:30 Sneak peek of Enzyme (automatic differentiation framework) 2:59:18 Questions and Future plans Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: 🤍
This webinar covers different Julia packages and programming models for working with GPUs, how to install and use them, and what tools are available to efficiently develop a GPU application or accelerate an existing code base. We will also focus on NVIDIA hardware using CUDA software, but solutions for other vendors will be mentioned as well. The webinar was be led by Dr. Tim Besard. He is a software engineer at Julia Computing, working on GPU support for the Julia language. He holds a Ph.D. in computer science engineering from Ghent University, Belgium, for research on abstractions to program hardware accelerators in high-level programming languages.
🤍 — Discussion & Comments: 🤍 — Presentation Slides, PDFs, Source Code and other presenter materials are available at: 🤍 — Computer system architecture trends are constantly evolving to provide higher performance and computing power, to support the increasing demand for high-performance computing domains including AI, machine learning, image processing and automotive driving aids. The most recent being the move towards heterogeneity, where a system has one or more co-processors, often a GPU, working with it in parallel. These kinds of systems are everywhere, from desktop machines and high-performance computing supercomputers to mobile and embedded devices. Many-core GPU has shaped by the fast-growing video game industry that expects a tremendous massive number of floating-point calculations per video frame. The motive was to look for ways to maximize the chip area and power budget dedicated to floating-point calculations. The solution is to optimize for execution throughput of a massive number of threads. The design saves chip area and power by allowing pipelined memory channels and arithmetic operations to have long latency. The reduce area and power on memory and arithmetic allows designers to have more cores on a chip to increase the execution throughput. In CPPCON 2018, we presented "A Modern C Programming Model for CPUs using Khronos SYCL", which provided an introduction to GPU programming using SYCL. This talk will take this further. It will present the GPU architecture and the GPU programming model; covering the execution and memory model. It will describe parallel programming patterns and common parallel algorithms and how they map to the GPU programming model. Finally, through this lens, it will look at how to construct the control-flow of your programs and how to structure and move your data to achieve efficient utilisation of GPU architectures. This talk will use SYCL as a programming model for demonstrating the concepts being presented, however, the concepts can be applied to any other heterogeneous programming model such as OpenCL or CUDA. SYCL allows users to write standard C code which is then executed on a range of heterogeneous architectures including CPUs, GPUs, DSPs, FPGAs and other accelerators. On top of this SYCL also provides a high-level abstraction which allows users to describe their computations as a task graph with data dependencies, while the SYCL runtime performs data dependency analysis and scheduling. SYCL also supports a host device which will execute on the host CPU with the same execution and memory model guarantees as OpenCL for debugging purposes, and a fallback mechanism which allows an application to recover from failure. — Gordon Brown Codeplay Software Principal Software Engineer, SYCL & C Edinburgh, United Kingdom Gordon Brown is a principal software engineer at Codeplay Software specializing in heterogeneous programming models for C. He has been involved in the standardization of the Khronos standard SYCL and the development of Codeplay's implementation of the standard from its inception. More recently he has been involved in the efforts within SG1/SG14 to standardize execution and to bring heterogeneous computing to C. — Videos Filmed & Edited by Bash Films: 🤍 *-* Register Now For CppCon 2022: 🤍 *-*
This simple program will display "Hello World" to the console. The screen output will be produced by the GPU instead of the CPU.
Computer Architecture, ETH Zürich, Fall 2020 (🤍 Lecture 25: GPU Programming Lecturer: Professor Onur Mutlu (🤍 Date: December 30, 2020 Slides (pptx): 🤍 Slides (pdf): 🤍
Come learn how to parallelize your code on with GPU programming on Google Colab. 🤍
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: 🤍
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: 🤍
Learn to use a CUDA GPU to dramatically speed up code in Python. 00:00 Start of Video 00:16 End of Moore's Law 01: 15 What is a TPU and ASIC 02:25 How a GPU works 03:05 Enabling GPU in Colab Notebook 04:16 Using Python Numba 05:40 Building Mandlebrots with and without GPU and Numba 07:49 CUDA Vectorize Functions 08:27 Copy Data to GPU Memory Tutorial: 🤍 Book: 🤍 If you enjoyed this video, here are additional resources to look at: Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: 🤍 O'Reilly Book: Practical MLOps: 🤍 O'Reilly Book: Python for DevOps: 🤍 Pragmatic AI: An Introduction to Cloud-based Machine Learning: 🤍 Pragmatic AI Labs Book: Python Command-Line Tools: 🤍 Pragmatic AI Labs Book: Cloud Computing for Data Analysis : 🤍 Pragmatic AI Book: Minimal Python: 🤍 Pragmatic AI Book: Testing in Python: 🤍 Subscribe to Pragmatic AI Labs YouTube Channel: 🤍 View content on noahgift.com: 🤍 View content on Pragmatic AI Labs Website: 🤍
Another session in a series of tutorials for the NCAR and university research communities featuring Jiri Kraus of NVIDIA as the speaker. 🤍
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 📚Related Lectures Jetson Orin Supercomputer - 🤍 Quick Deploy: Object Detection via NGC on Vertex AI Workbench Google Cloud - 🤍 Voice Swap using NVIDIA's NeMo - 🤍 🔴 Subscribe for more videos on CUDA programming 👍 Smash that like button, in case you find this tutorial useful. 👁🗨 Speak up and comment, I am all ears. 💰 Donate to help the channel Patreon - 🤍 BTC wallet - 3KnwXkMZB4v5iMWjhf1c9B9LMTKeUQ5viP ETH wallet - 0x44F561fE3830321833dFC93FC1B29916005bC23f DOGE wallet - DEvDM7Pgxg6PaStTtueuzNSfpw556vXSEW API3 wallet - 0xe447602C3073b77550C65D2372386809ff19515b DOT wallet - 15tz1fgucf8t1hAdKpUEVy8oSR8QorAkTkDhojhACD3A4ECr ARPA wallet - 0xf54bEe325b3653Bd5931cEc13b23D58d1dee8Dfd QNT wallet - 0xDbfe00E5cddb72158069DFaDE8Efe2A4d737BBAC AAVE wallet - 0xD9Db74ac7feFA7c83479E585d999E356487667c1 AGLD wallet - 0xF203e39cB3EadDfaF3d11fba6dD8597B4B3972Be AERGO wallet - 0xd847D9a2EE4a25Ff7836eDCd77E5005cc2E76060 AST wallet - 0x296321FB0FE1A4dE9F33c5e4734a13fe437E55Cd DASH wallet - XtzYFYDPCNfGzJ1z3kG3eudCwdP9fj3fyE #cuda #cudaprogramming #gpu
Guest lecture I gave at the University of Pennsylvania in October 2022, covering the WebGPU graphics API. Covers a wide range of WebGPU topics, including API overview, comparison with WebGL, best practices, and more. Slide deck: 🤍 Metaballs Demo: 🤍 Spookyball Game: 🤍 There were a few issues with the recording: Audio quality is hit-or-miss, and audio was not captured for the introduction before I started speaking (0:02:30). Some of the student's questions can't be heard clearly since they generally didn't have microphones. Also, the live demos did not get captured on the main video feed, though you can see them at the links above. Sorry!
💡Enroll to gain access to the full course: 🤍 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 the website for more learning material: 🔗 🤍 💻 ENROLL TO GET DOWNLOAD ACCESS TO CODE FILES 🔗 🤍 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 🤍 🧠 Use code DEEPLIZARD at checkout to receive 15% off your first Neurohacker order 👉 Use your receipt from Neurohacker to get a discount on deeplizard courses 🔗 🤍 👀 CHECK OUT OUR VLOG: 🔗 🤍 ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Tammy Mano Prime Ling Li 🚀 Boost collective intelligence by sharing this video on social media! 👀 Follow deeplizard: Our vlog: 🤍 Facebook: 🤍 Instagram: 🤍 Twitter: 🤍 Patreon: 🤍 YouTube: 🤍 🎓 Deep Learning with deeplizard: Deep Learning Dictionary - 🤍 Deep Learning Fundamentals - 🤍 Learn TensorFlow - 🤍 Learn PyTorch - 🤍 Natural Language Processing - 🤍 Reinforcement Learning - 🤍 Generative Adversarial Networks - 🤍 🎓 Other Courses: DL Fundamentals Classic - 🤍 Deep Learning Deployment - 🤍 Data Science - 🤍 Trading - 🤍 🛒 Check out products deeplizard recommends on Amazon: 🔗 🤍 🎵 deeplizard uses music by Kevin MacLeod 🔗 🤍 ❤️ Please use the knowledge gained from deeplizard content for good, not evil.
In this video we look at the basics of the GPU programming model! For code samples: 🤍 For live content: 🤍
Presented at the Argonne Training Program on Extreme-Scale Computing 2017. Slides for this presentation are available here: 🤍
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: 🤍
This is the introduction to how to do GPU programming in Rust with CUDA.
!!! Get the material discussed in the course and any other relevant information from here: 🤍 CSCS organized the course "GPU Programming with Julia", which took place online from November 2 to 5, 2021. The programming language Julia is being more and more adopted in High Performance Computing (HPC) due to its unique way to combine performance with simplicity and interactivity, enabling unprecedented productivity in HPC development. This course discussed both basic and advanced topics relevant for single and Multi-GPU computing with Julia. It focused on the CUDA.jl package, which enables writing native Julia code for GPUs. Day 1 00:00: Introduction to the course 05:02: General introduction to supercomputing (🤍 14:06: High-speed introduction to GPU computing (🤍 32:57: Walk through introduction notebook on memory copy and performance evaluation (🤍 Day 2: 1:24:53: Introduction to day 2 (🤍 1:39:12: Walk through solutions of exercise 1 and 2 (data "transfer" optimisations) (🤍 2:34:12: Walk through solutions of exercise 3 and 4 (data "transfer" optimisations and distributed parallelization) (🤍 Day 3: 03:31:57: Introduction to day 3 (🤍 03:32:59: Presentation of notebook 1: cuda libraries (🤍 04:24:31: Presentation of notebook 2: programming models (🤍 05:30:46: Presentation of notebook 3: memory management (🤍 06:03:48: Presentation of notebook 4: concurrent computing (🤍 Day 4: 06:27:15: Introduction to day 4 (🤍 06:28:13: Presentation of notebook 5: application analysis and optimisation (🤍 07:35:08: Presentation of notebook 6: kernel analysis and optimisation (🤍
Not clickbait!! These are the worst programming languages of all time. Does anyone like these languages, probably not since they're so bad. #python #java #javascript #coding #programming #dev #development
Register Free for NVIDIA's Spring GTC 2023, the #1 AI Developer Conference: 🤍 RTX 4080 Giveaway Form: 🤍 The talk about AI taking our programming jobs is everywhere. There are articles being written, social media going crazy, and comments on seemingly every one of my YouTube videos. And when I made my video about ChatGPT, I had two particular comments that stuck out to me. One was that someone wished I had included my opinion about AI in that video, and the other was asking if AI will make programmers obsolete in 5 years. This video is to do just that. And after learning, researching, and using many different AI tools over the last many months (a video about those tools coming soon), well let’s just say I have many thoughts on this topic. What AI can do for programmers right now. How it’s looking to progress in the near future. And will it make programmers obsolete in the next 5 years? Enjoy!! The Sessions I Mentioned: Fireside Chat with Ilya Sutskever and Jensen Huang: AI Today and Vision of the Future [S52092]: 🤍 Using AI to Accelerate Scientific Discovery [S51831]: 🤍 Generative AI Demystified [S52089]: 🤍 3D by AI: Using Generative AI and NeRFs for Building Virtual Worlds [S52163]: 🤍 Achieving Enterprise Transformation with AI and Automation Technologies [S52056]: 🤍 A portion of this video is sponsored by NVIDIA. 🐱🚀 GitHub: 🤍 🐦 Twitter: 🤍 💼 LinkedIn: 🤍 📸 Instagram: 🤍 📓 Learning Resources: My Favorite Machine Learning Course: 🤍 Open Source Computer Science Degree: 🤍 Python Open Source Computer Science Degree: 🤍 Udacity to Learn Any Coding Skill: 🤍 👨💻 My Coding Gear: My NAS Server: 🤍 My Hard Drives: 🤍 My Main Monitor: 🤍 My Second Monitor: 🤍 My Standing Desk: 🤍 My PC Build: 🤍 My AI GPU: 🤍
I'm very happy to be interviewing Troels Henriksen for this video. He's the lead of Futhark, a data-parallel functional programming language. It has support for running on GPU via CUDA and OpenCL back ends. Links: 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍
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.
This video is part of an online course, Intro to Parallel Programming. Check out the course here: 🤍
Title: An Introduction To GPU Programming Models Speaker: Jeff Larkin (NVIDIA Corporation) This talk will introduce you to several programming models for GPU programming and discuss why you may wish to use each. 0:00 Introduction 7:40 Lecture 41:10 Question and Answer
🤍ThePrimeagen made me learn Rust so you all don't have to. It's a beautiful language but, like, use it responsibly. ❤️ #rust #typescript ALL MY VIDEOS ARE POSTED EARLY ON PATREON 🤍 Everything else (Twitch, Twitter, Discord & my blog): 🤍
Support this channel via a special purpose donation to the Georgia Tech Foundation (GTF210000920), earmarked for my work: 🤍 This is the fourteenth lecture of the Summer 2020 offering of GPU Programming for Video Games at Georgia Tech. Tech ran all courses in a "distance learning" format this semester because of the Coronavirus. Since I was going to be recording these lectures anyway, I figured I'd stick them on youtube in case other folks find them useful. I also reused this lecture in later semesters. 0:00 Introduction 2:13 HLSL is simple 4:03 Uniform vs variable variables 5:37 Uniform variables 7:34 Semantics 9:40 Operators 10:29 Matrix multiplication 13:04 Library functions
MPAGS: High Performance Computing in Julia In this lecture, we talk about the concept of GPU programming, including the differences between GPU and CPU hardware. We discuss some models of how to compute on a GPU, with particular focus on CUDA and the CUDA.jl library. We cover some examples of the high-level array based programming mechanism provided by CUDA.jl to avoid the need to write one's own kernels. This is module designed for the Midlands Physics Alliance Graduate School (MPAGS). More information can be found on the website.
Support this channel via a special purpose donation to the Georgia Tech Foundation (GTF210000920), earmarked for my work: 🤍 This is the thirteenth lecture of the Summer 2020 offering of GPU Programming for Video Games at Georgia Tech. Tech ran all courses in a "distance learning" format this semester because of the Coronavirus. Since I was going to be recording these lectures anyway, I figured I'd stick them on youtube in case other folks find them useful. I also reused this lecture in later semesters. 0:00 Introduction 0:38 Programmable GPUs 4:28 Shader data 8:25 Specialized instructions 11:06 Why GPUs endure 12:51 Vertex shaders 15:11 Vertex data flow 18:51 Cross product example 24:31 Normalization example 26:08 Assembly instruction clarification 27:08 Simple pipeline example 29:33 Pixel shaders 31:06 Pixel data flow 32:39 Applications of pixel shaders 34:00 Motivating HLSL
First lecture from the course "Heterogeneous computing with performance modelling" which was given on 2020-11-(04-05) by HPC2N/SNIC/PRACE. Instructor and presenter: Mirko Myllykoski, HPC2N and Department of computing science at Umeå University, Umeå, Sweden. Course page: 🤍 Materials: 🤍
In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. Now these lectures and notes serve as a standalone book resource. 🤍 Chris Rackauckas, Massachusetts Institute of Technology Additional information on these topics can be found at: 🤍 and other Julia programming language sites Many of these descriptions originated on 🤍
Speaker: Mr. Oren Tropp (Sagivtech) "Prace Conference 2014", Partnership for Advanced Computing in Europe, Tel Aviv University, 13.2.14
Ближайшая конференция: С Russia 2023, 11–12 мая (Online), 23–24 мая (Offline) Подробности и билеты: 🤍 — — . . . The GPGPU ecosystem is rapidly developing thanks to the AI boom. To navigate this complex set of tools, we will compare the languages, libraries and compilers for GPGPU, learn how APIs for computing are different their graphics counterparts like OpenGL and Vulkan and look into caveats of writing concurrent programs in OpenCL & CUDA. The less common technologies we are going to cover are SyCL, Halide and BLAS libraries. To wrap things up, we will prepare four different combinations of tools and compare them against each other to make it simpler for you to pick the right set for your next project!
Foundation of GPU Programming - Filipe Mulonde - Meeting C 2022 Slides: 🤍 Survey: 🤍 Today's computers are heterogeneous systems composed of various types of processing units such as CPUs, GPUs, TPUS, etc. Graphics processing units (GPUs) can be used as general purpose parallel processors to make their excellent processing capabilities available for many workloads besides graphics. We'll talk about software hierarchy on GPUs. And we will give answers to the following questions: Where does GPU performance come from? What are the key hardware components that enable GPU performance? And if time permits, we will also introduce CUDA memory model. I hope this talk can help programmers to understand more about GPU and increase their confidence and comfort using GPUS by bringing knowledge of some of the main components that enable gpu performance. Topics Covered: 1. SIMD Processing and GPUs 2. DRAM bank-level parallelism 3. GPU Software Hierarchy 4. GPU Memory Hierarchy If time allows: 5. GPU programming model.
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![GPU programming with modern C++ - Michael Wong [ACCU 2019]](https://i.ytimg.com/vi/a_m440rZ5h8/mqdefault.jpg "GPU programming with modern C++ - Michael Wong [ACCU 2019]")
