An exploration of the differences between concurrency and parallelism in Go language

Exploration on the difference between concurrency and parallelism in Go language

In Go language, you often hear the concepts of concurrency and parallelism. Although these two words are often used interchangeably, they actually have different meanings. This article will explore the differences between concurrency and parallelism in the Go language, and use specific code examples to illustrate the differences between them.

First, let us take a look at the definitions of concurrency and parallelism:

• Concurrency (concurrency) refers to processing multiple tasks within a period of time, and there may be differences between these tasks. Not at the same time, but alternately to improve the responsiveness and efficiency of the system.
• Parallelism refers to processing multiple tasks at the same time. These tasks truly run on multiple processors at the same time to achieve faster processing speeds.

In Go language, concurrency is achieved through goroutine. Goroutine is a lightweight thread in the Go language. It is scheduled by the runtime system of the Go language and can achieve concurrent execution on a single thread. A goroutine can be created through the keyword go, so that the function can be executed in an independent goroutine.

Let us illustrate the difference between concurrency and parallelism through a simple example:

package main

import (
    "fmt"
    "runtime"
    "time"
)

func task(id int) {
    for i := 0; i < 5; i++ {
        fmt.Printf("Task %d: %d
", id, i)
        time.Sleep(time.Millisecond * 100)
    }
}

func main() {
    runtime.GOMAXPROCS(2) // 设置使用的最大CPU核心数

    go task(1)
    go task(2)

    time.Sleep(time.Second)
}

In the above code, we define two functions task, each The function prints the task information 5 times and pauses for 100 milliseconds after each print. In the main function, we start two goroutines through the go keyword to execute these two task functions. Finally, wait for 1 second through the time.Sleep function to ensure that the two goroutines have enough time to complete execution.

By running the above code, we can see that the tasks of the two goroutines are executed alternately instead of simultaneously. This is the concept of concurrency, although tasks are executed alternately on the same thread, they feel parallel in time because they occur at almost the same time.

In order to achieve parallelism, we can make some adjustments to the code:

package main

import (
    "fmt"
    "runtime"
)

func task(id int) {
    for i := 0; i < 5; i++ {
        fmt.Printf("Task %d: %d
", id, i)
    }
}

func main() {
    runtime.GOMAXPROCS(2) // 设置使用的最大CPU核心数

    go task(1)
    go task(2)

    // 等待任务完成
    fmt.Scanln()
}

In this modified code, we remove the time pause in the task function and pass fmt. The Scanln() function lets the program wait for user input. In this way, the two goroutine tasks will actually be executed at the same time, because they are not blocked by time pauses, which achieves a parallel effect.

Through this example, we can clearly see the difference between concurrency and parallelism. Concurrency improves efficiency by executing multiple tasks alternately on a single thread, while parallelism truly runs multiple tasks on multiple processors at the same time. In Go language, concurrency and parallelism can be easily achieved through goroutine and GOMAXPROCS functions.

In general, mastering the concepts of concurrency and parallelism is crucial to understanding the application of concurrent programming in the Go language. Only by deeply understanding the difference between the two can we better utilize the features of the Go language to write efficient concurrent programs.

Through the exploration of this article, I hope that readers will have a clearer understanding of the concepts of concurrency and parallelism in the Go language, and can also deepen their understanding of these two concepts through specific code examples. In actual Go language programming, the flexible use of concurrency and parallel technology will help improve the performance and efficiency of the program.

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