Existing GPU computing power

1. The main reason for GPU's strong computing power is that most of its circuits are arithmetic units. In fact, adders and multipliers are relatively small circuits. Even if they do many such operation units, they will not occupy too much chip area. And because other parts of GPU occupy a small area, it can also have more registers and caches to store data. On the one hand, CPU is so slow because it has a large number of units for processing other programs, such as branch loops, and because CPU processing requires a certain degree of flexibility, the structure of arithmetic logic unit of CPU is also much more complex. In short, in order to improve the processing speed of branch instructions, many components of CPU are used to do branch prediction, and correct and recover the results of Alu when the branch prediction error occurs. These greatly increase the complexity of the device
in addition, the current CPU design is also learning from GPU, that is, adding floating-point operation units with parallel computing and not so many control structures. For example, Intel's SSE Instruction set can perform four floating-point operations at the same time, and many registers have been added. In addition, if you want to learn GPU computing, you can download a CUDA SDK, which has very detailed instructions

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

3. The 2400g Vega has no video memory, but many mining tools can't run directly with such integrated graphics cards, and even if they can, it's useless. People can connect at least six pieces with a rx560 machine, but 2400g can't be used together at all. A 2400g must correspond to a motherboard, which is much higher than the cost of graphics card.

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.

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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