CUDA dynamometer

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. Both old and new occupations can be used as deputy occupations. There are five kinds of deputy occupations: medicine collecting, mining, alchemy, jewelry making, and equipment making. You can learn two kinds at the same time, but it must be one of the following three combinations. 1, But to learn alchemy, you must learn to collect herbs. 2. Mining + jewelry making (jewelry making is to make rings, belts, necklaces, shoulder pads, earrings, etc., but to learn jewelry making, you must learn mining). 3. Mining + equipment making (that is to make weapons, clothes, shields, etc., the premise is to learn mining). Specifically, you can go to craftsman street, and there is an NPC named Mei on the left, He also introced it (almost every map of craftsman street has an entrance. You can click "quick move" at the bottom of the small map to get in)

3.

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:

4. 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 .

5. 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.

6. Main board: gigabyte-g41 (more than 500 video memory)
memory: Kingston 2G
Display: aoc19 inch (self-made model)
hard disk: Western Digital 320
others are determined according to their own needs
I bought the whole machine here in Zhongshan City, Guangdong Province, and the price is about 2650 yuan (I think it's very cheap)<

it is recommended to buy second-hand ones online:
if you are interested, you can buy second-hand ones online. For example, I bought a cup (main frequency 2.2g), which costs 33 yuan (plus postage 9 yuan, a total of 42 yuan, it is estimated that the brand-new can buy about 300 yuan). With this cup, you can play large-scale games such as Tomb Raider 7, Roman total war and so on. It's no problem, super cost-effective! The memory and motherboard are easy to use, and they are ridiculously cheap. Here I will not give examples one by one. If you are interested, you can go online shopping, but you should also pay attention to the poor flexibility of online shopping, so you need to make more comparisons.

7. In short, it is a new graphic operation model, which defines new graphic operation method, development language and game image presentation. It can be said that the graphics card supporting this technology is certainly relatively new in technology, so it can be used to distinguish between new and old graphics cards. The following information is a simple and CUDA supporting graphics card. CUDA (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. From the composition of CUDA architecture, it includes three parts: development library, runtime environment and driver (Table 2). Development library is an application development library based on CUDA technology. At present, CUDA version 1.1 provides two standard mathematical operation libraries cufft (discrete fast Fourier transform) and cublas (discrete basic linear computation). These two mathematical operation libraries solve typical large-scale parallel computing problems, and they are also very common in intensive data computing. Developers can quickly and conveniently build their own computing applications on the basis of the development library. In addition, developers can also implement more development libraries based on CUDA technology. Runtime environment provides application development interface and runtime components, including the definition of basic data types and various functions such as calculation, type conversion, memory management, device access and execution scheling. The program code based on CUDA can be divided into two types in actual execution, one is the host code running on CPU, the other is the device code running on GPU. Different types of code run in different physical locations and can access different resources. Therefore, the corresponding runtime components are divided into three parts: common components, host components and device components, which basically include all the functions needed in the development of GPGPU and the resource interfaces that can be used, Developers can implement various types of computation through the programming interface of runtime environment. At present, there are many kinds of NVIDIA graphics cards with different GPU versions, and there are different differences between different GPU versions. Therefore, the driver part can basically be understood as the device Abstract Layer of CUDA enabled GPU, which provides the abstract access interface of hardware devices. CUDA provides run-time environment and realizes various functions through this layer. At present, the application based on CUDA development must have the hardware support of NVIDIA CUDA enable. Andy Keane, general manager of NVIDIA GPU computing division, said in an activity: a technology platform full of vitality should be open, and CUDA will also develop in this direction in the future. Due to the existence of hardware abstraction layer in CUDA architecture, it is possible to develop into a general GPGPU standard interface in the future, which is compatible with CUDA procts of different manufacturers; Toolkit is a C language development environment for GPU (graphics processor) supporting CUDA function. CUDA development environment includes: nvcc C language compiler, CUDA FFT and Blas library for GPU, analyzer, GDB debugger for GPU (alpha version released in March 2008), CUDA runtime driver (currently available in standard NVIDIA GPU driver), CUDA Programming Manual, and some specifications provided by CUDA Developer Software Development Kit (SDK) Example (with source code) to help users start CUDA Programming. These examples include: parallel al tone sorting, matrix multiplication, matrix transposition, performance evaluation by timer, prefix and (scan) of parallel large array, image convolution, one-dimensional DWT using Haar wavelet, OpenGL and Direct3D graphics interoperability examples, CUDA Blas and FFT library application examples, CPU-GPU C - and C + + - code integration, binomial option pricing model, Black Scholes Option pricing model · Monte Carlo option pricing model · parallel Mersenne twister (random number generation) · parallel histogram · image denoising · Sobel edge detection filter · MathWorks matlab & reg; Plug in (click here to download) the new SDK paradigm based on CUDA version 1.1 has also been released. To view the complete list and download the code, please click here. Technical function · provide standard C programming language on GPU · provide unified software and hardware solution for parallel computing on NVIDIA GPU supporting CUDA · CUDA compatible GPU includes many: from low-power notebook GPU to high-performance, multi GPU system· GPU supporting CUDA supports parallel data cache and thread execution manager. Standard FFT (fast Fourier transform) and Blas (Basic Linear Algebra Subroutine) numerical library. Special CUDA driver for computing. Optimized direct upload from CPU to GPU supporting CUDA Download channel. CUDA driver can interoperate with OpenGL and DirectX graphics drivers. It supports Linux 32-bit / 64 bit and windows xp 32-bit / 64 bit operating systems. For the purpose of research and development of language, CUDA provides direct access to drivers and assembly language level access

8. A programmable computing platform
programmers can easily write better game effects according to their own needs. Make the game more realistic possible.

9. CUDA CUDA (Compute Unified Device Architecture) is a computing platform launched by NVIDIA. With the development of graphics card, GPU is 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 card on g80 platform can use CUDA. 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 of CUDA SDK support windows and Linux systems, and can be integrated with Visual Studio 2005. At present, this technology is in its infancy, only supports 32-bit system, compiler does not support double precision data and other issues to 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. From the composition of CUDA architecture, it includes three parts: development library, runtime environment and driver (Table 2). Development library is an application development library based on CUDA technology. *** *** Developers can quickly and conveniently build their own computing applications on the basis of the development library. In addition, developers can also implement more development libraries based on CUDA technology. Runtime environment provides application development interface and runtime components, including the definition of basic data types and various functions such as calculation, type conversion, memory management, device access and execution scheling. The program code based on CUDA can be divided into two types in actual execution, one is the host code running on CPU, the other is the device code running on GPU. Different types of code run in different physical locations and can access different resources. Therefore, the corresponding runtime components are divided into three parts: common components, host components and device components, which basically include all the functions needed in the development of GPGPU and the resource interfaces that can be used, Developers can implement various types of computation through the programming interface of runtime environment
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10. During the meeting, the virus mainly attacked windows