Pipelining Meaning: Definition, Examples, and Translations
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pipelining
[ˈpaɪpəlaɪnɪŋ ]
Definitions
computer science
Pipelining is a technique used in computer architecture to increase the instruction throughput of a processor. It allows multiple instruction phases to be overlapped in execution, similar to an assembly line in manufacturing. This means that while one instruction is being executed, another can be decoded, and a third instruction can be fetched from memory. Pipelining enhances performance by reducing the time it takes to process subsequent instructions. It is particularly effective in RISC (Reduced Instruction Set Computing) architectures.
Synonyms
instruction overlap, throughput enhancement.
Examples of usage
- The new CPU design supports pipelining.
- Pipelining improves the overall performance of a processor.
- With pipelining, we can execute several instructions at once.
software development
In software development, pipelining refers to the practice of automating the process of transferring data between different stages of an application. This can include the integration of various tools and services where data flows from one stage to the next seamlessly. Pipelining can greatly enhance the speed of software release processes and improve the efficiency of continuous integration and continuous delivery (CI/CD) practices. It allows for real-time processing and reduces the manual effort in data handling.
Synonyms
automation process, data flow system.
Examples of usage
- We use pipelining in our CI/CD pipeline.
- Pipelining helps streamline the development process.
- Effective pipelining can lead to faster deployments.
Translations
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Interesting Facts
Computer Science
- Pipelining is used in CPUs to improve processing speed by overlapping the instruction execution phases.
- The technique allows a processor to work on new instructions while previous ones are still being completed, similar to how an assembly line works.
- RISC (Reduced Instruction Set Computer) architectures often utilize pipelining to maximize performance.
History
- The concept of pipelining was introduced in the 1950s as computers began to evolve beyond simple calculations.
- Early computers could only handle one instruction at a time, but advancements in technology allowed for this more efficient method.
- The first commercial microprocessors featuring pipelining were released in the 1970s, revolutionizing personal computing.
Engineering
- Pipelining isn't just for computers; it's used in various engineering fields to optimize workflows and process efficiencies.
- Manufacturing industries use pipelining principles to streamline production, reducing the time from raw material to finished product.
- In civil engineering, pipelines transport water, gas, and oil, reflecting the systematic movement and processing mirrored in computing.
Pop Culture
- Movies and video games often depict computers processing tasks in a 'pipelined' manner for dramatic effect, showing how fast they can run.
- In fictional narratives, hacking scenes frequently visualize 'pipelining' through rapid succession of data processing.
Education
- Teaching pipelining concepts often involves practical examples like cooking, where different dish components are prepared simultaneously.
- Understanding pipelining can help students in computer science better grasp performance optimization techniques in software engineering.
Origin of 'pipelining'
The term 'pipelining' originates from the concept of 'pipes' used in engineering and manufacturing, where materials are transported through connected tubes or channels. This analogy was adopted in computer science to describe the movement of instructions through various stages of execution. The application of pipelining in computer architecture began in the 1960s as a way to optimize processor performance by allowing multiple instructions to be processed simultaneously. Over the years, with advancements in technology, the concept of pipelining has evolved and is now prevalent in various fields such as software development and data processing. Today, it reflects a broader principle of efficiency and optimization by enabling parallel processing to achieve better performance in computing systems.