Concling Hash Speed from Difficulty and Block Frequency: Formula
The Ethereum network rely largely on its validators to maintain a safe and decentralized blockchain. One critical metric that affects the performance and stability of the network is the block frequency that denotes the speed at which new blocks are obtained. However, Hash speed calculation (the amount of computing power needed to approve transactions) can be a challenge without proper data. In this article, we will study how to infer the hash speed from difficulty and block the frequency using the formula.
Formula
To get the formula to infer the hash speed from difficulties and blocks of blocks, we need to understand that the hash speed is inverted proportional to the locking time (the time needed for one block). The more blocks are obtained per second, the faster the network can confirm transactions. Let’s divide the formula into two:
1
Difficulties : Difficulties reflect the level of calculation power needed to solve a mathematical problem, which in turn requires the amount of calculation power.
: The locking frequency is essentially the reverse part of the locking time (BFT^-1). This means that if more blocks are obtained per second, the network calculation power increases.
Using this understanding, we can get the formula to calculate the hash speed as follows:
Hash_rate = (Difficulties * BFTS) / Block_Frequency
Where:
– “Difficulties” is the level of computing power needed to solve mathematical problems.
– “BFTS” is the number of blocks obtained per second.
– “Block_Frequency” is the reverse time of the block, calculated by dividing 1 by block frequency.
Interpretation
This formula allows us to calculate hash speed based on the difficulties and locking frequency values. For example:
If the network difficulties are 10^18 (one trillion) and mine blocks at BFT = 100,000 blocks per second, we can assess the necessary computing power as hash_rate = (10^18 100 000) / bfts area
Calculation example
To show how this formula works in practice, let’s calculate the hypothetical hash speed of 0.1 tfhs (tera hashs per second), denoting a high -performance network with 10^12 blocks obtained per second:
Hash_rate = (10^18 * 100,000) / BFTS
Hash_rate ≈ 0.01 TFHS
In this case, the hash speed would be about 1 TFHS, stating that the network needs a huge amount of computing power to confirm transactions.
Conclusion
Understanding how the hash speed is associated with difficulty and the frequency of blocks, we can use the formula to assess the necessary computing power for different networks. This knowledge helps us to optimize network performance, ensure stability and maintain the integrity of the Ethereum blockchain.
