Cudagpu does not have enough computing power

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2. computing power is a concept put forward by NVIDIA when it released CUDA (Compute Unified Device Architecture, a programming language for GPU, similar to C programming for CPU). Because the graphics card itself is a floating-point computing chip, it can be used as a computing card, so the graphics card has computing power. Different graphics cards have different computing power. In order to show the difference, NVIDIA put forward the corresponding version of computing power x.x on the procts of different periods. Computing power 1.0 appeared on early graphics cards, such as the original 8800 ultra and many 8000 Series cards, as well as Tesla C / D / s870s cards. Cuda1.0 was released corresponding to these graphics cards. Today, computing power 1.0 has been eliminated from the market. Then there was computing power 1.1, which appeared on many 9000 Series graphics cards. Computing power 1.2 appears together with GT200 Series graphics card, while computing power 1.3 is proposed when upgrading from GT200 to GT200 A / b revision. In the future, there will be computing power 2.0, 2.1, 3.0 and other versions. The latest released version is computing power 6.1, which is supported by the latest Pascal architecture graphics card. At the same time, CUDA version is also updated to cuda8.0

ordinary users do not need to care about the computing power of the graphics card, only GPU programmers care about this problem when they write CUDA programs to develop GPU computing. As long as you know the model of your computer's graphics card, you can find the corresponding computing power https://developer.nvidia.com/cuda-gpus .

3.

It includes CUDA instruction set architecture (ISA) and parallel computing engine in GPU. Developers can now use C language to support CUDA; Architecture programming, C language is the most widely used high-level programming language. The program can then support CUDA & 8482; Runs at ultra-high performance on the processor. Other languages, including FORTRAN and C + +, will be supported in the future

with the development of graphics card, GPU becomes more and more powerful, and GPU optimizes the display image. It has surpassed the general CPU in computing. If such a powerful chip is only used as a graphics card, it would be too wasteful. Therefore, NVIDIA launched CUDA, which enables the graphics card to be used for purposes other than image computing

At present, only NVIDIA graphics cards on g80, G92, G94 and GT200 platforms can use CUDA, and the core of the toolkit is a C language compiler. G80 has 128 separate ALUs, so it is very suitable for parallel computing, and the speed of numerical calculation is much faster than CPU

The compiler and development platform in CUDA SDK support windows and Linux systems, and can be integrated with Visual Studio 2005

at present, this technology is in its infancy, which only supports 32-bit system, and the compiler does not support double precision data, which will be solved later. Geforce8cuda (Compute Unified Device Architecture) is a new infrastructure, which can use GPU to solve complex computing problems in business, instry and science. It is a complete GPGPU solution, which provides direct access interface to hardware instead of relying on graphical API interface to achieve GPU access

in the architecture, a new computing architecture is adopted to use the hardware resources provided by GPU, which provides a more powerful computing power than CPU for large-scale data computing applications. CUDA uses C language as programming language to provide a large number of high-performance computing instruction development capabilities, which enables developers to build a more efficient data intensive computing solution based on the powerful computing power of GPU< br />

4. Check with gpu-z, in fact, only n card supports CUDA now.

5. Very lack of setting, can't auto focus
Canon
EF
70-300 mm
F / 4-5.6l
is
USM
lens
Canon's fat white maximum aperture is only f4300 segment, only the poor f5.6. After adding twice the precision, the maximum aperture will actually decrease by two gears, that is, the maximum F8. Under such brightness, the focusing mechanism will not work,

6. Notebooks don't support changing graphics cards, so you have no choice but to change the computer

in addition, when selecting a machine, you must select a graphics card of gt630m or above. Otherwise, the performance is not enough

the simplest thing is to install a desktop computer.

7. CUDA computing power is mainly related to the hardware architecture of the graphics card. Some GPUs rece the hardware while the architecture remains unchanged, which makes the computing performance decline.

8. See here for the requirements of graphics card: http://www.nvidia.cn/object/cuda_ gpus_ Cn.html suggestion: 9600gso 384mb 192bit of Shuangmin G92 core is much better than other NVIDIA graphics cards of the same price, especially the GPU computing power, which is several times of GT220 and 9500GT of the same price. But it may be out of stock, and the power consumption is high. If you have 500 yuan, you can consider the 9600gso 384mb 192bit of Onda G92 core or the gddr5 of Onda gt240 512MB

9.

CUDA has two meanings

The first is CUDA core. That is CUDA core, NVIDIA graphics card from Fermi architecture began to use, Kepler architecture is also used. CUDA core is the most core part of the architecture, which is also the most dominant part in quantity. It is actually an ALU, which can also be called stream processor, and is the most basic operation unit of N card. Fermi architecture usually contains 48 cudacores in each group of SM units, while Kepler architecture contains 192 cudacores in each group of SMX. The number and scale of cudacore directly determine the operation scale of the graphics card, and also directly affect the performance of the graphics card. The number in "shaders" of N card in gpu-z is the number of CUDA cores (note only for Fermi and Kepler architectures). The following is a brief illustration of gk104 core, in which each green square represents a CUDA core. If you want to deeply understand the working principle of CUDA core, you also need to understand the architecture principle, which will not be introced here

the second is CUDA environment, CUDA full name, is a general computing architecture implemented by NVIDIA, and is also a kind of API like OpenCL and directcompute. Based on C language development, especially for GPU development of CUDA core architecture, it contains a variety of advanced general technologies, such as parallel architecture and so on. Program developers can use CUDA platform to achieve general computing of NVIDIA graphics card, and use GPU to participate in large-scale computing

CUDA computing is mostly used in professional fields, but not in consumer market. It's just that the salesperson is playing with this concept

10. What is GPU? It's an English phrase of GPU. Gt720 is a low-end graphics card, so it's natural to use GPU chip. CUDA is a general parallel computing architecture introced by NVIDIA, which enables GPU to solve complex computing problems.