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NVIDIA CUDA-X | NVIDIA Developer
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NVIDIA® CUDA-X, built on top of NVIDIA CUDA®, is a collection of libraries, tools, and technologies that deliver dramatically higher performance—compared to CPU-only alternatives— across multiple application domains, from artificial intelligence (AI) to high performance computing (HPC).
NVIDIA libraries run everywhere from resource-constrained IoT devices, to self-driving cars, to the largest supercomputers on the planet. As a result, you get highly-optimized implementations of an ever-expanding set of algorithms. Whether you’re building a new application or accelerating an existing application, NVIDIA libraries provide the easiest way to get started with GPU acceleration.
Components
Math Libraries
Parallel Algorithms
Image and Video Libraries
Communication Libraries
Deep Learning
Partner Libraries
GPU-accelerated math libraries lay the foundation for compute-intensive applications in areas such as molecular dynamics, computational fluid dynamics, computational chemistry, medical imaging, and seismic exploration.
cuBLAS
GPU-accelerated basic linear algebra (BLAS) library
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cuFFT
GPU-accelerated library for Fast Fourier Transforms
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CUDA Math Library
GPU-accelerated standard mathematical function library
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cuRAND
GPU-accelerated random number generation (RNG)
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cuSOLVER
GPU-accelerated dense and sparse direct solvers
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cuSPARSE
GPU-accelerated BLAS for sparse matrices
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cuTENSOR
GPU-accelerated tensor linear algebra library
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AmgX
GPU-accelerated linear solvers for simulations and implicit unstructured methods
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GPU-accelerated libraries of highly efficient parallel algorithms for several operations in C++ and for use with graphs when studying relationships in natural sciences, logistics, travel planning, and more.
Thrust
GPU-accelerated library of C++ parallel algorithms and data structures
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GPU-accelerated libraries for image and video decoding, encoding, and processing that leverage CUDA and specialized hardware components of GPUs.
nvJPEG
High performance GPU-accelerated library for JPEG decoding
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NVIDIA Performance Primitives
Provides GPU-accelerated image, video, and signal processing functions
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NVIDIA Video Codec SDK
A complete set of APIs, samples, and documentation for hardware-accelerated video encode and decode on Windows and Linux
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NVIDIA Optical Flow SDK
Exposes the latest hardware capability of NVIDIA Turing™ GPUs dedicated to computing the relative motion of pixels between images
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Performance-optimized multi-GPU and multi-node communication primitives.
NVSHMEM
OpenSHMEM standard for GPU memory, with extensions for improved performance on GPUs.
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NCCL
Open-source library for fast multi-GPU, multi-node communications that maximizes bandwidth while maintaining low latency.
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GPU-accelerated libraries for Deep Learning applications that leverage CUDA and specialized hardware components of GPUs.
NVIDIA cuDNN
GPU-accelerated library of primitives for deep neural networks
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NVIDIA TensorRT™
High-performance deep learning inference optimizer and runtime for production deployment
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NVIDIA Riva
Platform for developing engaging and contextual AI-powered conversation apps
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NVIDIA DeepStream SDK
Real-time streaming analytics toolkit for AI-based video understanding and multi-sensor processing
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NVIDIA DALI
Portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications
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OpenCV
GPU-accelerated open-source library for computer vision, image processing, and machine learning, now supporting real-time operation
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FFmpeg
Open-source multimedia framework with a library of plugins for audio and video processing
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ArrayFire
GPU-accelerated open source library for matrix, signal, and image processing
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MAGMA
GPU-accelerated linear algebra routines for heterogeneous architectures, by Magma
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IMSL Fortran Numerical Library
GPU-accelerated open-source Fortran library with functions for math, signal, and image processing, statistics, by RogueWave
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Gunrock
Library for graph-processing designed specifically for the GPU
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CHOLMOD
GPU-accelerated functions for sparse direct solvers, included in the SuiteSparse linear algebra package, authored by Prof
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Triton Ocean SDK
Real-time visual simulation of oceans, water bodies in games, simulation, and training applications, by Triton.
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CUVIlib
Primitives for accelerating imaging applications from medical, industrial, and defense domains
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Documentation
Training
Community
Members of the NVIDIA Developer Program get early access to all CUDA library releases and the NVIDIA online bug reporting and feature request system.
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What is a graphics processor? — Web programmer’s blog
- Details
- June 08, 2018
- Views: 7186
Computer components can certainly be complex technology, even for those who work directly with computers. Computers are made up of many individual components connected to do what you expect them to do, including running applications and sending data to other places.
The graphics processing unit (GPU) is just one of the parts that make a computer work.
Its primary duty is to ensure that content is displayed on your monitor screen. This includes everything from the user interface, to applications and web pages, and of course high intensity games.
What does a GPU do?
GPU became a popular term for the component that provided graphics on a computer in the 1990s, when chipmaker Nvidia coined it. GeForce graphics cards were the first to provide related technologies such as hardware acceleration, programmable shading, and threading.
While the task of rendering basic objects, such as the operating system environment, can usually be handled by the limited graphics processing capabilities built into the CPU, some more demanding workloads require additional power provided by a dedicated GPU.
In short, a graphics processor is a processor specially designed to handle complex graphics rendering tasks.
Computer graphics — such as in video games or other animated media — require each individual frame to be individually «drawn» by the computer, which requires a lot of power.
Most high-end desktop PCs have a dedicated graphics card that occupies one of the motherboard’s PCIe slots. They usually have their own memory allocation built into the card, which is reserved exclusively for graphics operations. Some particularly advanced PCs even use two GPUs connected together to provide even more processing power.
What are GPUs used for?
GPUs are most often used to drive high-quality gaming experiences, creating realistic digital graphics. However, there are also several business applications that rely on powerful graphics chips.
3D modeling software such as AutoCAD uses GPUs to render models. Since people who work with such software tend to make a few small changes in a short amount of time, the PC they’re on needs to quickly re-render the model.
Video editing is another common use case for powerful GPUs; while some powerful processors can handle basic video editing, if you’re working with a lot of high resolution files, in particular 4K or 360 video, a high performance GPU is needed to transcode files at a reasonable speed .
Graphics processing units (GPUs) are often preferred over conventional processors (CPUs) for use in machine learning, as they can process more features in a given period of time than processors. This makes them more suitable for building neural networks due to the amount of data they need to deal with.
However, not all GPUs are created equal — manufacturers such as AMD and Nvidia typically release specialized enterprise versions of their chips specifically designed for use in these applications and offer deeper support.
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Apple laptops: advantages and types of MacBook
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What is a Graphics Processing Unit (GPU)?
Articles
08/14/2021
Author: spear_1
When buying a new PC/laptop, it is very helpful to understand all the important internal components that make the computers actually work.
By knowing how they work, you can easily figure out if the computer in question has the right specs before you even make a purchase!
Today we’re going to take a look at a very important internal component of any PC: the GPU, or graphics processing unit. We will cover what it is, how it differs from a graphics card, and a few other details that are important to know about. Go!
Contents
- 1 What is a GPU?
- 2 Are graphics processors and video cards the same thing?
- 3 What is an integrated graphics processor?
- 4 What is a discrete GPU?
- 5 How do I know which graphics processor is installed in my computer?
- 6 Summary
What is a GPU?
GPU stands for Graphics Processing Unit, i.e. GPU . This is the type of processor that is responsible for graphics rendering (creating the images you see on the screen).
The GPU is essential for any computing task, such as 3D rendering and gaming.
Therefore, when buying a PC or laptop for gaming or creating various content, it is important to pay attention to the type of graphics processor, as this has a direct impact on picture quality and the speed of tasks.
Is a graphics processor and a video card the same thing?
In colloquial speech, the graphics processor is often used as a synonym for a video card. But technically this is not entirely correct.
The GPU is actually part of the graphics card. It is responsible for rendering graphics, and the graphics card provides the necessary power and access to high-speed memory.
In addition, the graphics card connects the GPU itself to other parts of the computer that require access to it (CPU, system memory, and storage) to perform desired tasks.
What is an integrated graphics processor?
iGPU is an integrated graphics processor. In other words, it is a GPU built into the central processing unit.
- Almost all Intel processors (with the exception of the F series) have integrated graphics, so they have an iGPU.
- only equips iGPUs with its line of hybrid processors (APUs).
AMD’s
With integrated graphics, the main processor shares memory with the graphics processor, reducing power consumption and overall PC load.
The integrated graphics processor is great for normal day-to-day tasks such as web browsing and word processing. But if you need a powerful computer for gaming or video editing, then you need to consider buying a device with a discrete graphics processor, or dGPU.
What is a discrete graphics processor?
dGPU — Discrete GPU. It is placed separately from the main processor and has its own dedicated memory, which is not transferred to the CPU.
Discrete graphics systems typically provide better performance and are able to handle more intensive tasks. But they also use more energy and generate more heat.
Some laptops have discrete graphics, but these processors are most commonly found on desktops.
How do I know which graphics processor is installed in my computer?
You can get information about the graphics processor on your computer in the device settings.
If you have Windows 10, you need to go to «Device Manager», then select «Display Adapters», where you will see a complete list of GPUs installed on your computer.
Then you need to google the name to see if the processor is embedded or discrete.
You can obtain more detailed information about your PC’s GPU and other specifications by other means, including using third-party programs.
Summary
Now you know a little more about GPUs. The main plus is that you can use this knowledge when buying a new PC / laptop.
Let’s go over the main points again:
- The graphics processor is an internal component of the computer responsible for rendering graphics.
