High Performance Computing: Break down complex tasks using Go WaitGroup

High performance computing: Use Go WaitGroup to decompose complex tasks

With the continuous improvement of computing power, we have more opportunities to face complex computing tasks. To take full advantage of the multi-core capabilities of modern computers, we need to break these tasks down into smaller, more independent subtasks and execute them simultaneously. The concurrency features of the Go language and the existence of the WaitGroup type allow us to achieve this goal easily.

Go language is a programming language with concurrency as the core design, and its concurrency model is built on goroutine and channel. Goroutine can be regarded as a concurrent execution body managed by the Go language scheduler, and can be understood as a lightweight thread. By using goroutine, we can decompose a task into multiple concurrently executed subtasks to achieve the effect of parallel computing.

However, concurrency does not mean parallelism. In the actual execution process, we need to wait for all subtasks to be completed before performing subsequent operations. This requires the use of the WaitGroup type.

WaitGroup is a synchronization primitive in the Go language used to coordinate multiple goroutines. It provides three main methods: Add, Done and Wait. The Add method is used to set the number of goroutines that need to be waited for, the Done method indicates that a goroutine has been executed, and the Wait method blocks the current goroutine until all goroutines are executed.

Below, we use a practical example to demonstrate how to use WaitGroup to decompose complex tasks. Suppose we have a task that requires calculating the Fibonacci sequence. The Fibonacci sequence is defined as: F(n) = F(n-1) F(n-2), where F(0)=0, F( 1)=1. We need to calculate the first n Fibonacci numbers.

First, we define a function fib to calculate the nth number of the Fibonacci sequence. Then, we create a WaitGroup variable wg and set the number of goroutines to wait to 1 by calling the Add method. Next, call the fib function in a goroutine and call the Done method after the calculation is completed. Finally, we block the main goroutine by calling the Wait method until all goroutines are executed.

package main

import (
    "fmt"
    "sync"
)

// 计算斐波那契数列的第n个数
func fib(n int) int {
    if n <= 1 {
        return n
    } else {
        return fib(n-1) + fib(n-2)
    }
}

func main() {
    n := 10

    // 创建WaitGroup变量
    var wg sync.WaitGroup

    // 设置需要等待的goroutine数量
    wg.Add(1)

    // 启动一个goroutine
    go func() {
        // 在goroutine中计算斐波那契数列的第n个数
        fmt.Printf("fib(%d) = %d
", n, fib(n))

        // 调用Done方法，表示goroutine已执行完毕
        wg.Done()
    }()

    // 阻塞主goroutine，直到所有的goroutine都执行完毕
    wg.Wait()
}

In the above code, we set the number of goroutines to wait to 1 by calling the Add method, and then calculate the nth number of the Fibonacci sequence in the started goroutine, and after the calculation is completed Then call the Done method. Finally, we call the Wait method to block the main goroutine until the calculation is completed.

In this way, we have successfully decomposed the complex Fibonacci calculation task into concurrently executed subtasks and used WaitGroup for coordination. In this way, we can make full use of the multi-core capabilities of modern computers and improve computing efficiency.

To sum up, the concurrency features and WaitGroup type of Go language provide us with a convenient and flexible way to decompose complex computing tasks and achieve high-performance parallel computing. In practical applications, we can adjust the concurrency granularity and task decomposition method according to the actual situation to achieve better performance and effects.

The above is the detailed content of High Performance Computing: Break down complex tasks using Go WaitGroup. For more information, please follow other related articles on the PHP Chinese website!