Golang process scheduling: Optimizing concurrent execution efficiency

Go process scheduling uses a cooperative algorithm. Optimization methods include: using lightweight coroutines as much as possible to reasonably allocate coroutines to avoid blocking operations and use locks and synchronization primitives

Go process scheduling: Optimizing concurrent execution efficiency

In Go, process scheduling is the process of deciding how to allocate CPU time to coroutines in a concurrent environment. Efficient process scheduling is critical to maximizing application performance and responsiveness.

Process Scheduling in Go

Go process scheduling is a cooperative scheduling algorithm based on the Linux system call sched_yield. This allows coroutines to actively give up time slices to other coroutines during function calls or channel communication operations.

Go's scheduler uses a scheduler model called M:N, where M represents the machine core and N represents the coroutine sequence running in parallel. Each M has a local run queue containing coroutines that are ready to run.

Optimize process scheduling

You can optimize process scheduling in Go through the following methods:

• Use lightweight as much as possible Coroutines: The cost of coroutines is very small, so please try to reduce the number of coroutines as much as possible.
• Reasonable allocation of coroutines: Ensure that each M has a similar number of coroutines.
• Avoid blocking operations: Blocking operations (such as file I/O or HTTP requests) will cause the coroutine to waste CPU time. Use non-blocking mode or Goroutine pooling to avoid these operations.
• Use locks and synchronization primitives: When synchronizing shared data, please use appropriate locks or synchronization primitives to minimize coroutine blocking.

Practical Case

Let us consider the following code, which processes integers in a list in parallel:

package main

import (
    "fmt"
    "sync"
    "sync/atomic"
)

const NumElements = 1000000

func main() {
    // 创建一个共享计数器
    var count uint64

    // 创建一个协程池
    var pool sync.WaitGroup
    pool.Add(NumElements)

    // 生成一个整数列表
    nums := make([]int, NumElements)
    for i := range nums {
        nums[i] = i
    }

    // 启动协程并行处理列表
    for _, num := range nums {
        go func(num int) {
            // 处理数字
            atomic.AddUint64(&count, uint64(num))
            pool.Done()
        }(num)
    }

    // 等待协程完成
    pool.Wait()

    // 汇总结果
    sum := atomic.LoadUint64(&count)
    fmt.Printf("The sum is: %d\n", sum)
}

In this example, we Use a coroutine pool and process integers in a list to optimize process scheduling. This minimizes blocking and improves concurrency.

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