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Synchronized random number generation in Go concurrent programming: Use a mutex (sync.Mutex) to control access to the rand.Rand random number generator. Each goroutine acquires the mutex before generating a random number and releases the mutex after generating it. This ensures that only one goroutine can access the random number generator at a time, eliminating data races.
In Go concurrent programming, it is sometimes necessary to generate random numbers across multiple goroutines. If synchronization is not taken care of, this can lead to race conditions and unpredictable behavior.
Go provides a math/rand
package to generate pseudo-random numbers. By default, it operates in a non-concurrency-safe manner. This means that if the same rand.Rand
instance is accessed concurrently, data races and indeterminate results will occur.
To synchronize random number generation in parallel processing, you can use a mutex (sync.Mutex
). A mutex allows only one goroutine to access the critical section at a time (in this case, the rand.Rand
instance).
The following code demonstrates how to use a mutex lock to synchronize random number generation:
package main import ( "math/rand" "sync" ) var ( // 全局互斥锁 randomLock sync.Mutex // 全局随机数生成器 randomGen *rand.Rand ) func init() { // 在程序启动时初始化随机数生成器 // 并设置随机种子 randomGen = rand.New(rand.NewSource(time.Now().UnixNano())) } func main() { // 创建一个等待组来跟踪 goroutine var wg sync.WaitGroup // 启动 10 个 goroutine 生成 10 个随机数 for i := 0; i < 10; i++ { wg.Add(1) go func(i int) { // 使用互斥锁保护随机数生成 randomLock.Lock() value := randomGen.Intn(100) randomLock.Unlock() // 打印生成的随机数 fmt.Printf("Goroutine %d: %d\n", i, value) wg.Done() }(i) } // 等待所有 goroutine 完成 wg.Wait() }
In this method, a global randomLock
mutex is used to protect the pairrandomGen
Access to the random number generator. Each goroutine acquires the mutex before generating a random number and releases the mutex after generating it. This ensures that only one goroutine has access to the random number generator at a time, eliminating data races.
In this way, random numbers can be generated safely and reliably in parallel processing.
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