GPU computing power can be adjusted
Main differences between CPU and GPU:
1. CPU is the central processing unit of computer
GPU is the graphics processor of computer CPU is a very large-scale integrated circuit, which includes Alu arithmetic logic unit, cache memory and bus CPU is the core of a computer's control and operation, its main function is to interpret the instructions issued by the computer and process the big data in the computer software GPU is the abbreviation of image processor. It is a kind of microprocessor which is specially used for PC or embedded device to perform image operation The work of GPU is similar to that of CPU mentioned above, but not entirely. It is designed to perform complex mathematical and geometric calculations. This game has high requirements in this respect, so many game players also have deep feelings for GPU 
so CPU and GPU are two completely different things, they just sound the same
extended data:
CPU and GPU are different in design because they are used to deal with different tasks at first, and some tasks are similar to the problems that GPU is used to solve at first, so we use GPU to calculate. The operation speed of GPU depends on how many pupils are employed, and the operation speed of CPU depends on how powerful professors are employed, The professor's ability to deal with complex tasks is very good, but for less complex tasks, it still can't hold many people
of course, today's GPU can also do some slightly complicated work, which is equivalent to upgrading to the level of junior high school students and senior high school students, but it still needs CPU to feed data to the mouth before it can start to work, which is still managed by CPU
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 .
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 2005at 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 />
some people say that the performance of GPU is 40 times that of CPU, which is not comprehensive.
if we just say that the performance of GPU is 40 times that of CPU in parallel and intensive floating-point operations, this may be feasible. (I don't think it's so exaggerated. It's amazing that the best GPU can achieve 10 times that of the best CPU. Moreover, now CPU has multi-core, This greatly improves the CPU operation, and GPGPU seems to be limited to single core, but it is groundless in full operation.
in fact, it is still very difficult to use GPU as a general processor, and the most important thing is that GPU is specially designed to process graphics, so its programming language architecture and programming environment are difficult to be universal. When GPU runs non graphic programs, it often needs to rely on extremely complex algorithms and more tortuous processes. The powerful computing potential of GPU is often exhausted in such a circuitous process
in addition, e to the lack of unified API and driver support, GPU program developers have to develop corresponding software versions for each GPU architecture, which makes it more difficult to promote GPU as a common processor project.
Yes, when NVIDIA designs and selects models, Ti has better performance than no ti. It can also be said that GPU has strong processing power. Sometimes in detail analysis, sometimes without ti is better. For example, in the figure below, the acceleration frequency and basic speed of Ti are better, but the overall performance of Ti is much better
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