GPU computing power

1. As the name suggests, the mine calculation card is a graphics card used for mining. More strictly speaking, it is a graphics card used for mining with high load for a long time. The graphics card used in mining usually works at full load 24 hours a day for several months. In this way, PCB and electronic components will accelerate aging, affecting the life of components. Not counting the rest time of the graphics card, even if we play the game for 8 hours a day, the life of the mine card is only one third of that of the normal graphics card. It can be said that the general life of the mine card is only a few months.

2. The unit of mining efficiency is generally hash / s, that is, the amount of hash value that can be obtained per second. GPU can reach the level of megahash / s.
gtx1060 mining computing power reaches 18.7mh/s, which is a little behind rx470 mining computing power of 22.4mh/s

3. Suggestions: first, the performance of this graphics card is OK, and the low-end graphics card that can share memory with an independent graphics card must be
Second, even if the performance is shared, it will not improve much

4. GPU is the meaning of graph line processor, the original design intention is to do graphics computing. GPU chip is essentially a collection of many small CPUs for image processing. Of course, each CPU can also do general operations such as addition, subtraction, multiplication, division or non shift jump, so it can also be used for mining. When GPU is mining, it wastes less chip area and power consumption than CPU, so mining efficiency is higher than CPU
What about ASIC? It is a more optimized circuit than the CPU mentioned above. There is no specific definition, but there is a rule in the field of chip design: the more general computing platform, the less efficient it is to complete specific computing. ASIC is the most dedicated computing platform. If we understand this, we can come to such a basic conclusion: in the mining field, if we use the computing power proof mechanism, as long as the CPU can dig, ASIC can dig. No matter what algorithm is used, ASIC can dig. There is no reason that CPU can dig, but ASIC can't, So the biggest difference between GPU and ASIC is their specific computing power.

5. The same power consumption, that is, the common CPU and GPU within 100 W, the graphics card is gtx1050ti power consumption of 75 W, like gtx1060 power consumption of 130 W, the CPU can be i7 six core 12 thread level, or the CPU performance is dedicated to data processing, the performance is relatively strong,
if you exclude the power consumption, the high-end single chip computing performance has already exceeded the CPU speed.

6.

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7. That is, specific performance / power consumption. GPU is much better than CPU. At the beginning, it was precisely because CPU had to spend a lot of power consumption and transistors on control circuit and cache that GPU was made into independent chip, and further GPGPU.

8.

CPU: central processing unit, is a very large scale integrated circuit, is a computer operation core (core) and control unit (control unit). Its main function is to interpret computer instructions and process data in computer software

GPU: graphics processor, also known as display core, visual processor and display chip, is a kind of microprocessor which is specialized in image operation on personal computers, workstations, game machines and some mobile devices (such as tablet computers, smart phones, etc.)

CPU and GPU are designed for two different application scenarios respectively

1. CPU needs strong versatility to deal with different data types, and at the same time, it also needs logical judgment, which will introce a lot of branch jump and interrupt processing. All these make the internal structure of CPU extremely complex

GPU is faced with highly unified, interdependent large-scale data and pure computing environment without interruption

extended data

application direction of CPU and GPU

GPU is suitable for high predictability and a large number of similar operations, as well as high latency and high throughput architecture operations

9.

GPU server is a fast, stable and flexible computing service based on GPU, which is used in video codec, deep learning, scientific computing and other scenarios

the function is: excellent graphics processing ability and high-performance computing ability, providing extreme computing performance, effectively liberating the computing pressure, and improving the computing processing efficiency and competitiveness of procts