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Golang process scheduling: Optimizing concurrent execution efficiency
Golang process scheduling: Optimizing concurrent execution efficiency
Apr 03, 2024 pm 03:03 PM
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() {
// 創(chuàng)建一個(gè)共享計(jì)數(shù)器
var count uint64
// 創(chuàng)建一個(gè)協(xié)程池
var pool sync.WaitGroup
pool.Add(NumElements)
// 生成一個(gè)整數(shù)列表
nums := make([]int, NumElements)
for i := range nums {
nums[i] = i
}
// 啟動(dòng)協(xié)程并行處理列表
for _, num := range nums {
go func(num int) {
// 處理數(shù)字
atomic.AddUint64(&count, uint64(num))
pool.Done()
}(num)
}
// 等待協(xié)程完成
pool.Wait()
// 匯總結(jié)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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