The concept of graphics computing power

1.

21.5 * 3600 seconds * 24 hours = 1857600 M / day
1857600 / 1024 / 1024 = 1.7715 T / day
a t of computing power / day can dig bitcoin worth 4.03 yuan
1.7715 * 4.03 = 7.14 yuan / day

since 2017, bitcoin has reached new heights. Yesterday (May 23), the value of bitcoin broke through the ￥ 16000 mark, hyping virtual currency, becoming a matter that the general public can participate in, And a little bit of technical level, both fried money, but also began to have the technical content of mining

as we all know, virtual currency has a numerical peak, the more difficult it is to dig. In fact, it's hard to judge whether to lose money or not if we continue to dig. Therefore, in the end how to dig, dig what can be the fastest back to this, this paper studies the above two core issues based on the current market situation

2. If there is a graphics card, according to the article I saw before, at this stage, it is obvious that it is fast to dig Zec and other currencies
the biggest advantage of XmR is that it can use CPU to dig holes and revitalize rendant CPU resources... Using graphics card to dig XmR, according to the current price, it may not be able to recover the cost!

3. 1. Geforce GTX 1070 graphics card
overall power consumption: 220W
2, computing power: 25.7m
graphics card price: 2899 yuan
number of eth g every 24 hours: 0.017
revenue generated every 24 hours: 28.84 yuan
expected payback time: 100.52 days

4. 1. According to the mining software, the larger the value, the faster the "speed" in the software

2. Different mining softwares have different algorithms and ranking.

5. According to a certain software, the value is tested. The larger the value is, the faster the speed will be in the software
it is common in mining software, but the ranking will be different for different algorithms.

6. N card directly ignores
a card, that is to say, it depends on the frequency and SP
,
the better the a card, the stronger the mining.

8. Because of its architecture, the computing power of the graphics card is far stronger than that of the CPU. Some software or features developed for the graphics card can call the computing power of the graphics card, thus greatly improving the efficiency of computing
mainly used in scientific computing, video rendering, video processing and other fields

in our daily use of computers, we seldom use the general computing power of graphics card, which has little impact.

9. 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 .