Shulou(Shulou.com)06/03 Report--

Today, I would like to talk to you about the difference between parallel computing and distributed computing. Many people may not know much about it. In order to make you understand better, the editor has summarized the following for you. I hope you can get something from this article.

1. Parallel computing invests more machines, the data size remains the same, and computing speed is faster, while distributed computing invests more machines and can handle larger data.

2. Parallel computing must require time synchronization, while distributed computing has no time limit.

Parallel computing

Parallel computing (Parallel Computing) refers to the process of using multiple computing resources to solve computing problems at the same time, and it is an effective means to improve the computing speed and processing power of computer systems. Its basic idea is to use multiple processors to solve the same problem cooperatively, and the problem to be solved is divided into several parts, each of which is calculated in parallel by an independent processor. A parallel computing system can be either a specially designed supercomputer with multiple processors or a cluster of independent computers interconnected in some way. The data is processed by the parallel computing cluster, and then the result of the processing is returned to the user.

Parallel computing can be divided into temporal parallelism and spatial parallelism.

Time parallelism: refers to the assembly line technology, for example, when the factory produces food, the steps are divided into:

1. Cleaning: rinse the food clean.

2. Disinfection: disinfect food.

3. Cut: cut food into small pieces.

4. Packaging: put food in a bag.

If the assembly line is not used, one food will not be processed until the above four steps are completed, which is time-consuming and affects efficiency. But with assembly line technology, four foods can be processed at the same time. This is the time parallelism in the parallel algorithm, which starts two or more operations at the same time, which greatly improves the computing performance.

Spatial parallelism: refers to the concurrent execution of computing by multiple processors, that is, connecting more than two processors through the network to simultaneously calculate different parts of the same task, or large-scale problems that can not be solved by a single processor.

For example, Xiao Li is going to plant three trees in Arbor Day. If Xiao Li needs six hours to complete the task, Arbor Day called in his good friends Xiao Hong and Xiao Wang. The three people began to dig holes and plant trees at the same time. Two hours later, each of them completed a tree planting task. This is the spatial parallelism in the parallel algorithm, dividing a large task into multiple identical sub-tasks to speed up problem solving.

Distributed computing

Generalized definition

Study how to divide a problem that requires a great deal of computing power into many small parts, then assign these parts to many computers for processing, and finally synthesize these calculation results to get the final result.

Recent distributed computing projects have been used to use the idle computing power of the computers of thousands of volunteers around the world to analyze electrical signals from outer space and find hidden black holes through the Internet. and explore the possible existence of extraterrestrial intelligent life; can find more than 10 million digits of Mason prime; can also find and find more effective drugs against HIV. These projects are huge and require an astonishing amount of computation, and it is impossible for a single computer or individual to complete the calculation in an acceptable time.

Definition of the Chinese Academy of Sciences

Information is shared between two or more software that can run either on the same computer or on multiple computers connected through a network. Compared with other algorithms, distributed computing has the following advantages:

1. Rare resources can be shared.

2. Through distributed computing, the computing load can be balanced on multiple computers.

3. You can put the program on the computer that is most suitable for running it.

Among them, sharing rare resources and balancing load is one of the core ideas of computer distributed computing.

After reading the above, do you have any further understanding of the difference between parallel computing and distributed computing? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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