What do algorithm and computing power mean
computing power is a measure of bitcoin network processing power. That is, the speed at which the computer calculates the output of the hash function. Bitcoin networks must perform intensive mathematical and encryption related operations for security purposes. For example, when the network reaches a hash rate of 10th / s, it can perform 10 trillion calculations per second
in the process of getting bitcoin through "mining", we need to find its corresponding solution M. for any 64 bit hash value, there is no fixed algorithm to find its solution M. we can only rely on computer random hash collisions. How many hash collisions can a mining machine do per second is the representative of its "computing power", and the unit is written as hash / s, This is called workload proof mechanism pow
computing power provides a solid foundation for the development of big data, and the explosive growth of big data poses a huge challenge to the existing computing power. With the rapid accumulation of big data in the Internet era and the geometric growth of global data, the existing computing power can no longer meet the demand. According to IDC, 90% of the global information data is generated in recent years. And by 2020, about 40% of the information will be stored by cloud computing service providers, of which 1 / 3 of the data has value
therefore, the development of computing power is imminent, otherwise it will greatly restrict the development and application of artificial intelligence. There is a big gap between China and the advanced level of the world in terms of computing power and algorithm. The core of computing power is the chip. Therefore, it is necessary to increase R & D investment in the field of computing power to narrow or even catch up with the gap with the developed countries in the world
unit of force
1 KH / S = 1000 hashes per second
1 MH / S = 1000000 hashes per second
1 GH / S = 1000000000 hashes per second
1 th / S = 100000000000 hashes per second
1 pH / S = 100000000000 hashes per second
1 eh / S = 100000000000 hashes per second
of
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What does 1p mean
first of all, 1p computing power is equivalent to about 1.05 million g, which means that if you have 1g computing power of the whole network, you can get almost 1.05 million of bitcoin output of the whole network. According to the output of 3800 bitcoins per day, we can see that the daily income of 1g of computing power has dropped to 0.0036 bitcoins, which is about 2.7 yuan according to the current market price. If the electricity cost and mining machinery hardware cost are included, the profit is almost gone
secondly, 1p's whole network computing power seems amazing, but in fact, in a year's time, you will think it's just a pediatrics, because cointera will launch 2p miner in December, and bitmine will launch 4P miner in March next year. If these companies are not put in biochemical weapons by Syria, it should be expected that bitcoin's whole network computing power will reach more than 10p in a year's time, By then, 1g of computing power will only be able to dig 0.00036 bitcoins a day.
computing power refers to computing power, refers to that in the process of getting bitcoin through "mining", we need to find its corresponding solution M. for any 64 bit hash value, there is no fixed algorithm to find its solution M. we can only rely on computer random hash collisions. How many hash collisions can a mining machine do per second, is the representative of its "computing power". The unit is written as hash / s, which is the so-called proof of work mechanism (POW)
in the process of getting bitcoin through "mining", we need to find its corresponding solution M. for any 64 bit hash value, there is no fixed algorithm to find its solution M. we can only rely on the computer's random hash collisions. How many hash collisions can a mining machine do per second is the representative of its "computing power", and the unit is written as hash / s, This is the so-called proof of work mechanism (POW)
recently, bitcoin's network computing power has entered the era of P computing power (1P = 1024t, 1t = 1024g, 1g = 1024m, 1m = 1024k). In the ever-increasing computing power environment, the arrival of P era means that bitcoin has entered a new stage of arms race
computing power is a measure of the total computing power of the unit that generates new blocks under certain network consumption. The single blockchain of each coin varies with the time required to generate a new transaction block.
for example, all kinds of mining machines sold by digyi mining have high computing power and low energy consumption, so you can consult.
algorithm is the specific method and steps to solve the problem, so it has the following properties:
1. Finiteness: an algorithm must ensure that the execution of finite steps ends (if the steps are infinite, the problem can not be solved)
2. Accuracy: the steps must be clear and what to do
3. Input: the conditions we have before solving the problem
4. Output: the output is the answer we need
5. Feasibility: logic cannot be wrong, steps must be limited, and results must be obtained
generally speaking, algorithm is the method and step to solve the problem. It existed before the invention of the computer. But after the invention of the computer, its application has become more extensive. Through a simple algorithm, the use of computer computing speed, can make the problem simple
for example, calculate 1 × two × three × 4 × nine hundred and ninety-nine million nine hundred and ninety-nine thousand nine hundred and ninety-nine × 1000000000
if you calculate manually, you can imagine that even if you use the paper of N truck, it is difficult to calculate, and even if you do, it is difficult to guarantee its accuracy
if we use VB algorithm:
dim a as integer
A = 1
for I = 1 to 1000000000
A = a * I
next I
input a
in this way, a simple algorithm, through the powerful computing power of the computer, the problem is solved
the explanation of this algorithm: every time I multiply, its value will increase by 1 until 1000000000. In this way, every number from 1 to 1000000000 will be multiplied. And every time you multiply, you assign the end to a, so that a represents all the results of the previous multiplication up to 1000000000. The final a is what we want
〓 the following is copied from the network. If you have enough patience, you can refer to it
algorithm is a series of clear instructions to solve problems, that is to say, it can obtain the required output for a certain standard input in a limited time. If an algorithm is defective or not suitable for a problem, executing the algorithm will not solve the problem. Different algorithms may use different time, space or efficiency to complete the same task. The advantages and disadvantages of an algorithm can be measured by space complexity and time complexity
an algorithm can be understood as a complete problem-solving step composed of basic operations and specified operation sequence. Or it can be regarded as a finite exact calculation sequence designed according to the requirements, and such steps and sequences can solve a class of problems
an algorithm should have the following five important characteristics:
1. Finiteness: an algorithm must ensure that it finishes after executing finite steps
2. Accuracy: every step of the algorithm must have an exact definition
3. Input: an algorithm has 0 or more inputs to describe the initial condition of the operation object. The so-called 0 input means that the algorithm itself determines the initial condition
4. Output: an algorithm has one or more outputs to reflect the results of processing the input data. The algorithm without output is meaningless
5. Feasibility: in principle, the algorithm can run accurately, and it can be completed after people do a limited number of operations with pen and paper
computer scientist Nicholas worth once wrote a famous book "data structure ten algorithms = programs", which shows the status of algorithms in the field of computer science and computer application
[edit this paragraph] algorithm complexity
the same problem can be solved by different algorithms, and the quality of an algorithm will affect the efficiency of the algorithm and even the program. The purpose of algorithm analysis is to select appropriate algorithm and improve algorithm. The evaluation of an algorithm mainly considers time complexity and space complexity
time complexity
the time complexity of an algorithm refers to the time resources consumed by the algorithm. Generally speaking, a computer algorithm is a function f (n) of the problem size n, so the time complexity of the algorithm is recorded as
t (n)= Ο( F (n))
therefore, the larger the scale of the problem n is, the higher the growth rate of algorithm execution time is, which is called asymptotic time complexity
space complexity
the space complexity of an algorithm refers to the space resources consumed by the algorithm. Its computation and representation are similar to that of time complexity, which is generally expressed by the asymptotic property of complexity. Compared with the time complexity, the analysis of space complexity is much simpler
see the Internet entry & quot; Algorithm complexity & quot< [edit this paragraph] the basic method of algorithm design and analysis
1. Recursive method
recursive method is a method to solve a problem by using a recursive relationship of the problem itself. It divides the problem into several steps and finds out the relationship between the adjacent steps so as to achieve the goal. This method is called recurrence method< 2. Recursion
recursion refers to a process: a function constantly references itself until the referenced object is known
3. Exhaustive search method
exhaustive search method is to enumerate and test many candidate solutions that may be solutions one by one in a certain order, and find those qualified candidate solutions as the solutions of the problem
4. Greedy method
greedy method is a method which does not pursue the optimal solution, but only hopes to get a more satisfactory solution. Greedy method can get satisfactory solution quickly, because it saves a lot of time to find the optimal solution. Greedy method often makes the best choice based on the current situation, and does not consider all kinds of possible overall situation, so greedy method does not backtrack
5. Divide and conquer method
divides a complex problem into two or more identical or similar subproblems, and then divides the subproblems into smaller subproblems... Until the subproblems can be solved simply and directly, and the solution of the original problem is the combination of the solutions of the subproblems< Dynamic programming is a method used in mathematics and computer science to solve optimization problems with overlapping subproblems. The basic idea is that the original problem is decomposed into similar subproblems, and the solution of the original problem is obtained by solving the subproblems. The idea of dynamic programming is the basis of many algorithms, which is widely used in the field of computer science and engineering
7. Iterative method
iteration is a process of solving problems (generally solving equations or equations) by finding a series of approximate solutions from an initial estimate in numerical analysis. The methods used to realize this process are collectively referred to as iterative method< Algorithms can be roughly divided into basic algorithm, data structure algorithm, number theory and algebra algorithm, computational geometry algorithm, graph theory algorithm, dynamic programming and numerical analysis, encryption algorithm, sorting algorithm, retrieval algorithm, randomization algorithm and parallel algorithm
[edit this paragraph] examples
there are many classic algorithms, such as: & quote; Euclidean algorithm & quot<
[edit this paragraph] classic monograph on algorithms
there are many books on Algorithms in the market, among which the most famous are the art of computer programming and introction to algorithms
[edit this paragraph] the history of the algorithm
"algorithm" is the Chinese name of the algorithm in mainland China, which comes from Zhoubi Suanjing; The English name algorithm comes from Persian mathematician al khwarizmi in the 9th century, because al khwarizmi proposed the concept of algorithm in mathematics“ Algorithm "was & quot; algorism"， It means the arithmetic of Arabic numerals, which evolved into & quot; in the 18th century; algorithm" Euclidean algorithm is considered to be the first algorithm in history. The first program was written by ADA Byron in 1842 to solve Bernoulli equation for Babbage analyzer, so ADA Byron is considered to be the first programmer in the world. Because Charles Babbage failed to complete his Babbage analyzer, this algorithm could not be implemented on the Babbage analyzer. Because & quot; well-defined procere" Because of the lack of precise definition in mathematics, mathematicians and logicians in the 19th and early 20th century had difficulties in defining algorithms. Turing, an English mathematician in the 20th century, put forward the famous Turing thesis and put forward a hypothetical abstract model of computer, which is called Turing machine. The emergence of Turing machine solves the problem of algorithm definition, and Turing's idea plays an important role in the development of algorithm.
1. Algorithm refers to the accurate and complete description of the solution, is a series of clear instructions to solve the problem, the algorithm represents a systematic method to describe the strategy mechanism to solve the problem. That is to say, it can get the required output in a limited time for a certain standard input. If an algorithm is defective or not suitable for a problem, executing the algorithm will not solve the problem
different algorithms may use different time, space or efficiency to complete the same task. The advantages and disadvantages of an algorithm can be measured by space complexity and time complexity
Calculation theory is the principle in the process of calculation, which refers to the way of thinking in the process of calculation, and is to solve the problem of why to calculate in this way. For example, the calculation of 214 + 35 is based on the composition of the number: 214 is composed of 2 hundreds, 1 ten and 4 ones, 35 is composed of 3 tens and 5 ones, so first add 4 ones and 5 ones to 9 ones, then add 1 ten and 3 tens to 4 tens, and finally combine 2 hundreds, 4 tens and 9 ones to 249, which is the calculation principle
extended data:
algorithm commonly used design patterns
1, complete traversal method and incomplete traversal method: when the solution of the problem is a finite discrete solution space, and the correctness and optimality can be verified, the simplest algorithm is to completely traverse all elements in the solution space, one by one to detect whether the elements are the solutions we want
this is the most direct algorithm, and its implementation is often the simplest. However, when the solution space is very large, this algorithm may lead to an unbearable amount of computation in engineering. In this case, incomplete traversal methods, such as various search methods and planning methods, can be used to rece the amount of computation
Divide and Conquer: divide a problem into independent parts and solve them separately. One of the advantages of this solution is that it is convenient for parallel computing Dynamic programming method is often used when the global optimal solution of the problem is composed of local optimal solution Greedy algorithm: a common approximate solution. When the global optimal solution of the problem is not (or cannot be proved to be) composed of local optimal solutions, and there is no requirement for the optimality of the solution, it can be used as a method Degeneracy: a method to simplify a problem into an equivalent or approximate, relatively simple model through logical or mathematical reasoning, and then solve ithowever, the FM band antenna is not a magnetic rod antenna, but a rod antenna outside the machine, and also has a loop composed of a hollow coil inctance (tens or even hundreds of times the size of the medium wave antenna) and a variable capacitor to complete the tuning. Because the hollow inctance is too small, the rod antenna is responsible for enhancing the signal reception, So when you listen to FM, you find that the antenna has something to do with FM.
a matching antenna should be equivalent to a resistance, for example, the half wave oscillator is about 73 Ω， The equivalent oscillator is about 300 Ω