Ethereum: Understanding the gap between the average speed and performance in the real world

As a user of the popular Blockchain Ethereum, you are probably not a stranger to the concept of block time and various factors that can affect its speed. However, have you ever wondered why the average speed of Etherum Node (MHPS or MHz) may not be exactly 800 MHP on websites such as Bitminter?

In this article, we will immerse ourselves in potential explanations of the difference between befriending and the actual performance of a single BFL node operating on the Bitmintter client.

Understanding the average speed

The average speed is calculated by taking the total number of transactions that can be processed per second (TPS) and divided it by 60 (because it is 60 seconds per hour). This gives us an idea of ​​how effectively network nodes can operate transactions. Higher average speed means better performance, but has not necessarily increased revenues.

Specifications on BFL

Before we examine possible explanations, let’s look at the specifications in a single BFL node:

• Clock speed: 2.3 GHz

• Cache memory: 64 GB

• Number of processor cores: 8

• Total computing units (here): 16

Although these specifications are impressive, there are several factors that can affect the actual knot performance.

explanation of the gap

What could be due to the difference between the advertised rate and actual performance?

• Network overload: If you run the knot in a crowded network (e.g. due to the high load of other nodes or no bandwidth), it can lead to slower speeds. In addition, if you use a Bitminter customer who is intended for Ethereum, the connection may not be optimized for low delay transactions.

• Transaction complexity: If the number of transactions processed per second increases, the performance of your node will fall accordingly. For example, if the average transaction size is relatively small (e.g. 1-10 bytes), he won to process them, but with the increase in the size of the transaction, the processing time increases.

• Energy consumption: Single BFL nodes are intended for high -performance calculations, which cost: energy consumption. Starting a node with higher clock speeds and more computing units may require more energy, especially if you use low -power equipment.

• heat generation: Because the node generates heat due to the high clock speed and computing units, it can lead to increased temperature, reducing the efficiency of the entire system.

• Software optimization: Bitmintte client cannot be optimized for BFL nodes, which may cause a slower response time or reduced bandwidth.

Application

Although there are several factors, it is likely that the combination of these explanations is responsible for the difference between your unfavorable indicator and actual performance. To optimize the node performance:

• Make sure you run in a well -ventilated environment to minimize heat production.

• Update the Bitminter customer to the latest version to ensure compatibility with BFL nodes.

• Think about modernizing the equipment to reduce energy consumption and heat production.

• Regularly monitor your node performance to identify optimization areas.

Understanding these factors, you can take steps to improve the overall performance and revenues of your node. Happy extraction!