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Nvidia CUDA in 100 Seconds



What is CUDA? And how does parallel computing on the GPU enable developers to unlock the full potential of AI? Learn the …

Title: Harnessing the Power of Nvidia CUDA: A Game-Changer in Modern Computing

In the rapidly evolving world of technology, Nvidia's CUDA (Compute Unified Device Architecture) continues to be a significant player, particularly in the realm of high-performance computing and artificial intelligence (AI).

First introduced in 2006, CUDA has revolutionized the way complex computations are handled, allowing for the execution of multiple tasks simultaneously on graphics processing units (GPUs). This parallel processing capability has made CUDA an indispensable tool in various fields, from scientific research to gaming and data centers.

Recently, the relevance of CUDA has been underscored by the surge in AI applications. With AI models becoming increasingly complex, the need for efficient and powerful computing solutions has grown exponentially. CUDA, with its ability to handle massive amounts of data and perform complex calculations in real-time, is at the forefront of this revolution.

For instance, deep learning, a subset of AI, relies heavily on CUDA. Deep learning models require significant computational resources to train, and GPUs, powered by CUDA, can process these computations much faster than traditional CPUs. This speedup has led to breakthroughs in areas such as image recognition, natural language processing, and autonomous vehicles.

Moreover, the ongoing push towards energy-efficient computing has also highlighted the importance of CUDA. GPUs, thanks to their parallel processing capabilities, are generally more energy-efficient than CPUs for certain tasks, making CUDA a key player in the quest for greener computing.

In summary, Nvidia's CUDA continues to be a game-changer in modern computing, particularly in the context of AI and energy-efficient computing. Its ability to handle complex computations efficiently and its role in driving AI innovation make it a crucial technology to watch in the coming years.

This information is not entirely new, as CUDA has been in use for over a decade. However, its continued relevance and impact, particularly in the context of AI and energy-efficient computing, remain significant and worth highlighting.


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37 Comments

  • homsexuals deserve equal rights!!!!!!!!!!@#!@#

  • HEY! Just noticed… no "Forth in 100 seconds" yet? Get Forth. … I hear it's "Uber-Chad" and one of slim few languages that can claim to be simple.

  • " ass I'm doing here in visual studio" goes on to open Vs code😭

  • Actually there is way less matrix and way more vector operations with a fixed widths.

  • How to compare NVIDIA cuda core performance with AMD cards? Does it have any correlation there?

  • Don't want to be that guy. But you don't need an Nvidia GPU. AMD has them as well???

  • Fuck NVIDIA. Fuck AMD for ditching OpenGL. ROCm sucks. I hope someone working in AMD reads this.

  • Has anyone else questioned how these are 100 seconds? I’m not complaining but the premise is a lie!!! Keep the lies coming Fireship!

  • CUDA is some cracking name πŸ˜‚πŸ˜‚. Will the next generation of CUDA become CUDAI?

    Computing
    Unified
    Device
    Architecture
    Intergrated

  • now you can invest 100 days to install it on ubuntu

  • Gemini 1.5 Pro: This video is about Nvidia CUDA, a parallel computing platform that allows programmers to use the GPU for more than just playing video games.

    The video starts by explaining what GPUs are typically used for. GPUs are used for computing graphics. When you play a game in 1080p at 60 FPS, there are over 2 million pixels on the screen that may need to be recalculated after every frame. This requires a lot of matrix multiplication and vector transformations, which GPUs are very good at. Unlike a CPU that has a few cores, a modern GPU like the RTX 4090 has over 16,000 cores. This makes GPUs perfect for tasks that can be parallelized.

    Cuda allows developers to tap into the power of the GPU. Data scientists all around the world are using Cuda to train machine learning models. To use Cuda, you first need to write a function called a Cuda kernel that runs on the GPU. Then you copy some data from your main RAM to the GPU's memory. The CPU will then tell the GPU to execute that function or kernel in parallel. The code is executed in a block which itself organizes threads into a multi-dimensional grid. Then the final result from the GPU is copied back to the main memory.

    The video then goes through an example of building a Cuda application. The first step is to install the Cuda toolkit which includes device drivers, a runtime compiler, and developer tools. The code is written in C++. The video shows an example of a function that adds two vectors together. Because billions of operations might be happening in parallel, the code needs to calculate the global index of the thread in the block that it's working on. The video also talks about using managed memory which can be accessed from both the CPU and the GPU.

    The video then shows a main function for the CPU that runs the Cuda kernel. The code uses a for loop to initialize arrays with data and then passes this data to the function to run it on the GPU. The video also talks about configuring the Cuda kernel launch to control how many blocks and threads are used to run the code in parallel. This is important for optimizing multi-dimensional data structures like tensors used in deep learning. Finally, the code uses Cuda device synchronize to wait for the GPU to finish executing the code and then copies the data back to the host machine.

  • The more you buy, the more you save.
    Eventually you save more than you spend in time, you return to the past.

  • #KARRAT business partner with #NVIDIA massive pump around the corner πŸ’₯πŸ’₯πŸ’₯πŸ’₯ soon 50$ for one coin πŸš€πŸš€πŸš€πŸš€πŸš€ #KARRAT project number one definitely soon massive blast πŸŒ‹πŸŒ‹πŸŒ‹πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ˜±πŸ˜±πŸ˜±πŸ˜±πŸ˜±πŸ˜±πŸ˜±πŸ˜±πŸ˜±πŸ˜±πŸŽ‰πŸŽ‰πŸŽ‰πŸŽ‰πŸŽ‰β€

  • I can't believe humans come from hunting dinosaurs and live in caves and now some people have to deal with massive complicated math to bring technology to us

  • "cu" in brazilian portuguese is that hole… inside the ass kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk

  • Idk if this is a hot take or not. I know nvidia GPUs are super expensive… but if you look into just how much effort it takes to design and build one, both from a software and hardware side, it doesnt seem that wild to me really.

  • What, if I want to calculate 32768 tasks in parallel? How is this managed?

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TechTrends Tom

TechTrends Tom

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Welcome to the world of TechTrends Tom, where technology meets adventure and current events shape tomorrow’s innovations. Tom is a passionate blogger with an insatiable curiosity for the latest in tech, the thrill of exploration, and the pulse of the news cycle. His blog is a dynamic space where readers can dive into cutting-edge tech reviews, follow his adventurous exploits around the globe, and stay informed on the events that are transforming our world.

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