Gpu c: GPU-Z Graphics Card GPU Information Utility

1 января, 2023

Code Generation and GPU Support
— MATLAB & Simulink

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Generate portable C/C++/MEX functions and use GPUs to deploy or
accelerate processing

Audio Toolbox™ includes support to accelerate prototyping in MATLAB® and to generate code for deployment.

GPU Code Acceleration.  To speed up your code while prototyping, Audio Toolbox includes functions that can execute on a Graphics
Processing Unit (GPU). You can use the gpuArray (Parallel Computing Toolbox) function
to transfer data to the GPU and then call the gather (Parallel Computing Toolbox) function to
retrieve the output data from the GPU. For a list of Audio Toolbox functions that support execution on GPUs, see Function List
(gpuArray support). You need
Parallel Computing Toolbox™ to enable GPU support.

C/C++ Code Generation.  After you develop your application, you can generate portable
C/C++ source code, standalone executables, or standalone
applications from your MATLAB code. C/C++ code generation enables you to run your
simulation on machines that do not have MATLAB installed and to speed up processing while you work in
MATLAB. For a list of Audio Toolbox functions that support C/C++ code generation, see
Function List
(C/C++ Code Generation). You need MATLAB
Coder™ to generate C/C++ code.

GPU Code Generation.  After you develop your application, you can generate optimized
CUDA® code for NVIDIA® GPUs from MATLAB code. The code can be integrated into your project as
source code, static libraries, or dynamic libraries, and can be used
for prototyping on GPUs. You can also use the generated CUDA code within MATLAB to accelerate computationally intensive portions of
your MATLAB code in machine learning, deep learning, or other
applications. For a list of Audio Toolbox functions that support GPU code generation, see
Function List
(GPU Code Generation). You need MATLAB
Coder and GPU Coder™ to generate CUDA code.

Apps

MATLAB Coder Generate C code or MEX function from MATLAB code
GPU
Coder		 Generate GPU code from MATLAB code

Functions

codegen Generate C/C++ code from
MATLAB code
gather Transfer distributed array or gpuArray to local workspace
gpuArray Array stored on GPU

Topics

  • Generate C Code at the Command Line (MATLAB Coder)

    Generate C/C++ code from MATLAB code by using the codegen command.

  • Run MATLAB Functions on a GPU (Parallel Computing Toolbox)

    Supply a gpuArray argument to automatically run functions
    on a GPU.

  • Prerequisites for Deep Learning with MATLAB Coder (MATLAB Coder)

    Install products and configure environment for code generation for deep learning
    networks.

  • GPU Computing Requirements (Parallel Computing Toolbox)

    Support for NVIDIA GPU architectures.

  • Recognize and Display Spoken Commands on Android Device (Simulink Support Package for Android Devices)

    This example shows how to use the Simulink® Support Package for Android™ Devices to deploy a deep learning algorithm that recognizes and displays commands spoken through your Android device such as a phone or tablet.

Related Information

Featured Examples

Speech Command Recognition Code Generation with Intel MKL-DNN Using Simulink

Use Embedded Coder® in Simulink® and Intel® MKL-DNN to deploy feature extraction and a convolutional neural network for
speech command recognition on Intel processors.

Speech Command Recognition on Raspberry Pi Using Simulink

Deploy feature extraction and a convolutional neural network for speech command
recognition on Raspberry Pi™.

Accelerate Audio Deep Learning Using GPU-Based Feature Extraction

Leverage GPUs for feature extraction to decrease the time required to train an audio
deep learning model.

Keyword Spotting in Noise Code Generation on Raspberry Pi

Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction on Raspberry Pi™. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.elf) file on Raspberry Pi. Communication between MATLAB® (.mlx) file and the generated executable file occurs over asynchronous User Datagram Protocol (UDP). The incoming speech signal is displayed using a timescope. A mask is shown as a blue rectangle surrounding spotted instances of the keyword, YES. For more details on MFCC feature extraction and deep learning network training, visit Keyword Spotting in Noise Using MFCC and LSTM Networks.

Keyword Spotting in Noise Code Generation with Intel MKL-DNN

Generate code to spot keywords using a Bidirectional Long Short-Term Memory (BiLSTM)
network and mel frequency cepstral coefficient (MFCC) feature extraction.

Speech Command Recognition Code Generation with Intel MKL-DNN

Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition on Intel® processors. To generate the feature extraction and network code, you use MATLAB® Coder™ and the Intel® Math Kernel Library for Deep Neural Networks (MKL-DNN). In this example, the generated code is a MATLAB executable (MEX) function, which is called by a MATLAB script that displays the predicted speech command along with the time domain signal and auditory spectrogram. For details about audio preprocessing and network training, see Speech Command Recognition Using Deep Learning.

Speech Command Recognition Code Generation on Raspberry Pi

Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition to Raspberry Pi™. To generate the feature extraction and network code, you use MATLAB Coder™, MATLAB® Support Package for Raspberry Pi Hardware, and the ARM® Compute Library. In this example, the generated code is an executable on your Raspberry Pi, which is called by a MATLAB script that displays the predicted speech command along with the signal and auditory spectrogram. Interaction between the MATLAB script and the executable on your Raspberry Pi is handled using the user datagram protocol (UDP). For details about audio preprocessing and network training, see Speech Command Recognition Using Deep Learning.

Acoustics-Based Machine Fault Recognition Code Generation with Intel MKL-DNN

Generate a MATLAB standalone executable for acoustics-based machine fault recognition.

Acoustics-Based Machine Fault Recognition Code Generation on Raspberry Pi

Generate a Raspberry Pi standalone executable for acoustics-based machine fault recognition.

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GPU Programming | Coursera

What you will learn

  • Develop CUDA software for running massive computations on commonly available hardware

  • Utilize libraries that bring well-known algorithms to software without need to redevelop existing capabilities

  • S​tudents will learn how to develop concurrent software in Python and C/C++ programming languages.

  • S​tudents will gain an introductory level of understanding of GPU hardware and software architectures.

Skills you will gain

  • Machine Learning
  • GPU
  • Parallel Computing
  • Image Processing
  • C++
  • Cuda
  • Python Programming
  • Thread (Computing)
  • Algorithms
  • C/C++
  • Nvidia
  • Data Science

About this Specialization

5,669 recent views

This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.

L​earners will complete at least 2 projects that allow them the freedom to explore CUDA-based solutions to image/signal processing, as well as a topic of choosing, which can come from their current or future professional career. They will also create short demonstrations of their efforts and share their code.

Shareable Certificate

Shareable Certificate

Earn a Certificate upon completion

100% online courses

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Intermediate Level

At least 1 year of computer programming experience, preferrably with the C/C++ programming language.

Hours to complete

Approximately 5 months to complete

Suggested pace of 4 hours/week

Available languages

English

Subtitles: English

Shareable Certificate

Shareable Certificate

Earn a Certificate upon completion

100% online courses

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Intermediate Level

At least 1 year of computer programming experience, preferrably with the C/C++ programming language.

Hours to complete

Approximately 5 months to complete

Suggested pace of 4 hours/week

Available languages

English

Subtitles: English

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.

Instructor

Chancellor Thomas Pascale

Instructor and Software Engineer

Computer Science

3,335 Learners

4 Courses

Offered by

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Frequently Asked Questions

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  • What background knowledge is necessary?

  • Do I need to take the courses in a specific order?

  • Will I earn university credit for completing the Specialization?

  • What will I be able to do upon completing the Specialization?

More questions? Visit the Learner Help Center.

Why GPUs are needed and where they are used

GPUs are also used in KVM — special programs for gamers, for example, Playkey belongs to them. They allow you to run games with good graphics on low-power computers by transferring the load to the cloud. So a powerful computer is not required.

GPUs and Heavy Computing

Heavy computing is a computation that uses complex algorithms and consumes a lot of resources. An example of such calculations is docking. This is a molecular modeling method, it allows you to choose a molecule that interacts best with the desired protein.

This is labor-intensive and expensive work, for example, in the USA it takes an average of $985 million to develop one new drug. By using GPUs, pharmaceutical companies save on computing power, speed up development, and thus spend less money.

At the beginning of the pandemic, scientists from Moscow State University began looking for substances that could be useful in the treatment of coronavirus. To find a cure, they picked up a promising protein, analyzed its structure, and created models for docking. Molecular modeling was launched on the Lomonosov supercomputer.

Another example of heavy computation is the analysis of a large amount of heterogeneous data. For example, it is required when processing seismographic data.

In regions where oil has been produced for a long time, standard seismic survey methods no longer cope with the search for deposits in the required volumes. So, for example, it happened in Bashkortostan, where the first well appeared in the 1930s.

Therefore, for the exploration of oil reserves in LLC SPC «Geostra» used cloud solutions. The calculations were carried out on the VK Cloud platform (former MCS). For complex calculations, NVIDIA Tesla V100 GPUs were used. The pilot project proved to be successful: it was possible to predict the efficiency of future wells and determine drilling locations.

GPUs and the Industrial Internet of Things

In industrial plants, smart sensors collect data on the operation of equipment and transmit it to an analytical system. Using this information, companies can monitor equipment performance, predict breakdowns, plan preventive maintenance and think about optimizing production. GPUs are used to process data faster.

For example, WaveAccess based on the VK IoT Platform has developed solutions for the Unified Data Collection and Analysis Platform, with the help of which the state controls nature management. There are four solutions in total: air monitoring system, remote monitoring of cultural heritage sites, illegal logging and overgrowing of agricultural land. The platform collects data using IoT sensors and detects incidents in real time. Based on these data, state authorities conduct inspections.

IoT solutions are also used to create digital twins — virtual copies of machines or entire factories. In this case, the system does not just analyze data from smart sensors, but builds a three-dimensional equipment model based on them. In fact, engineers on the computer see how this or that machine works.

Data transfer and processing takes time. Therefore, enterprises that need to know about problems in real time use GPUs to speed up work.

For example, thanks to the digital twin of the Moscow CHPP-20 , it was possible to increase the efficiency of the enterprise by 4%. Another example is the virtual prototype of the plant KAMAZ , where almost 50 machine tools were digitized, as well as manipulators, production robots and other equipment. Thanks to this, the company can control all stages of car assembly.

At the Siemens plant in Amberg, 12 million programmable logic controllers are produced per year, that is, one product per second. The company has combined virtual and real production: the products are marked with codes that transmit to the equipment its route and requirements for each operation — special programs monitor the process. As a result, new orders at the plant are completed in a day, 99.99885% of manufactured products fully comply with quality standards, and the cost price has decreased by 25%.

Heavy-duty Cloud Computing

You don’t have to buy GPUs to increase your computing speed — you can rent power from a cloud provider. The GPU in the cloud has several features:

  • Fast connection of capacities. No need to wait for hardware deliveries, the provider connects GPUs on request.
  • Possibility of renting for a short period. If you need to perform complex calculations one-time, then you can connect the GPU only for this time, and then immediately disconnect it.

NVIDIA Tesla V100 GPUs can be connected to virtual machines on the VK Cloud (formerly MCS) platform. This is one of the latest generations of GPUs, each processor has 640 tensor cores. GPUs are connected to the desired virtual machine upon request, for this you need to contact technical support.

There are other machine learning and big data solutions on the platform. Using them, you can build your own analytical system or a solution for training neural networks in the cloud.

What is the difference between a GPU and a video card?

Gaming peripherals and hardware

10/22/2022

Those who are new to the PC world often drown in all sorts of terms and abbreviations, which, in fairness, are sometimes really confusing! In addition, some people even believe that all these elements are interchangeable.

One of the most common examples is the confusion between the terms «video card» and «graphics processing unit». So, are graphics cards and GPUs the same thing? Below you will find the answer to this and several other questions.

Video Card vs. GPU

The GPU, short for GPU (Graphics Processing Unit), is a specialized processor dedicated to graphics processing. Since the chip is designed and optimized specifically for such tasks, it is much more efficient than the CPU and handles most of the workload when it comes to game graphics.

As for the video card , it does not only consist of a GPU, as it also includes a number of other parts, such as video memory, circuit board, connectors and a cooler. However, a video card is hardware designed for graphics processing and video output in general.

So «GPU» refers specifically to the graphics chips manufactured by NVIDIA, AMD, and Intel, while «Video Card» refers to the end product you are purchasing. Video cards are usually manufactured by partner companies such as ASUS, MSI, Gigabyte, EVGA, etc.

A few other terms

A graphics card is also sometimes referred to as a discrete card. This indicates that the graphics card is separate hardware that normally communicates with the rest of the computer using a PCIe slot on the motherboard.

Meanwhile, the term «external video card» describes a conventional discrete graphics card that is not installed in the computer case, but is connected to it with a cable, usually through a Thunderbolt 3 port. Most often, people use external devices with laptops, as they help maintain portability while boosting gaming performance to near-desktop levels.

Next, we have integrated or integrated video card. This is a graphics processor that is integrated into the computer along with the CPU, that is, this type of model has the cores of the central processing unit and the graphics processor on the same chip. Integrated video cards do not take up space on the motherboard and are more energy efficient, but they do not have their own memory, which is why they use the system RAM.

As a result, integrated graphics are far from being as powerful as discrete graphics cards, which is why they are not at all suitable for games. However, they are more than capable of handling basic graphics-related tasks. Considering that these types of models help to save space, energy and money, it is obvious that they are great for everyday tasks such as web browsing, video, music playback, etc. etc.

The term «hybrid processor» or APU (accelerated processor unit, APU) also appears here. Basically, it’s just a marketing term coined by AMD that indicates that the processor comes with integrated graphics.

However, AMD’s hybrid Ryzen models are some of the most powerful integrated graphics cards out there, and are ideal for gaming if you’re building an entry-level PC and want to play at lower resolutions and/or lower settings.