CUDA reserved computing power

1. The computing power of the project, gtx1080ti, is 6.1 / 3.7, about 1.64 times that of K80
at present, the popular cards for deep learning are rtx2080ti and rtx2070 (multi-channel), and it is not easy to buy more than one of them when gtx1080ti is delisted Second hand another)
* for the deep learning of CUDA platform, the graphics card mainly focuses on: single precision floating-point operation, video memory, tensor core (Turing architecture and volt architecture, RTX series and Titan V)
* in terms of the main stability and some special functions of Tesla, double precision (currently this deep learning is less used), single precision and half precision floating-point operation have little advantage, It's expensive (if you want to surpass gtx1080ti, you need tens of thousands of Tesla V100)

2. Hello, you should be talking about the so-called computing power of some blockchain platforms. Now, the algorithms of these platforms are uneven. The real blockchain platform in China is actually zero. This computing power is calculated according to the user's activity and some other statistical rates.

3. Graphics computing power will not decline, but the difficulty of mining will rise, interested in digging fun net to see.

4. Introction: the former name of dark coin, a kind of counterfeit bitcoin, focuses on anonymous function. There are many innovations, such as master node network, fast payment, mixed currency anonymous technology, proposals can vote, and developers have fixed budget support. The core consists of a unique incentive P2P network. Miners are rewarded for maintaining blockchain security; The main node holders are rewarded for verifying transactions, storing data and providing a variety of services. Time of issue: 2014 maximum supply: 18900000 dash total circulation: 7875498dash market value: 5186548619

5.

data sources: the following information comes from enterprise credit reference institutions, more detailed enterprise risk data, company official website, company profile, which can be inquired on nailing enterprise dictionary, and more company recruitment information can be inquired on the company official website< br />

• Company profile:

Shenzhen Lianrong Technology Co., Ltd. was established on July 6, 2017, with registered capital of 0.7 and legal representative of Li Weiping. Its address is 14 / F, Minsheng financial building, Haitian Road, Lianhua Street, Futian District, Shenzhen. Its unified social credit code and tax number are 91440300ma5elwky67 and its instry is information technology consulting service, The registration authority is Shenzhen market supervision and Administration Bureau, and its business scope is licensed business items: design and sales of computer network equipment, communication equipment and electronic procts; Database and computer network services; The development and application of blockchain technology; Supply chain management and related supporting services; Accept the entrustment of enterprises to engage in information and technical services; Enterprise management consulting; Domestic trade; Import and export business Items prohibited by laws and administrative regulations are excluded; The business registration number of Shenzhen Lianrong Technology Co., Ltd. is 440300201734294; Branches:

, 8226; Foreign investment:

,

, 8226; Shareholders:

&; Senior executives:

6. 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 .

7. The gt610m is actually an overclocking version of the gt520m. It is an entry-level graphics card and low-end
number of shaders: 48unified
manufacturing process: 40nm
grating unit: 4
bit width: 64bit
capacity: 2048m
computing power:
pixel fill rate: 1.7gpixel/s
texture fill rate: 6.8gtexel/s
video memory bandwidth: 12.8gb
hope to help you.

8.

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 />

9. It should be forward compatible, except that new instructions cannot be used.