Why is the performance of `moving_avg_concurrent2` not improving with increased concurrency, despite splitting the list into smaller chunks processed by individual goroutines?

Why does the performance of moving_avg_concurrent2 not improve with the increase of concurrent execution?

moving_avg_concurrent2 splits the list into smaller pieces and uses a single goroutine to handle each piece. For some reason (it's not clear why), this function using one goroutine is faster than moving_avg_serial4, but using multiple goroutines starts to perform worse than moving_avg_serial4.

Why moving_avg_concurrent3 is much slower than moving_avg_serial4?

The performance of moving_avg_concurrent3 is worse than moving_avg_serial4 when using a goroutine. Although increasing num_goroutines can improve performance, it is still worse than moving_avg_serial4.

Even though goroutines are lightweight, they are not completely free, is it possible that the overhead incurred is so large that it is even slower than moving_avg_serial4?

Yes, although goroutines are lightweight, they are not free. When using multiple goroutines, the overhead of launching, managing, and scheduling them may outweigh the benefits of increased parallelism.

Code

Function:

// 返回包含输入移动平均值的列表（已提供，即未优化）
func moving_avg_serial(input []float64, window_size int) []float64 {
    first_time := true
    var output = make([]float64, len(input))
    if len(input) > 0 {
        var buffer = make([]float64, window_size)
        // 初始化缓冲区为 NaN
        for i := range buffer {
            buffer[i] = math.NaN()
        }
        for i, val := range input {
            old_val := buffer[int((math.Mod(float64(i), float64(window_size))))]
            buffer[int((math.Mod(float64(i), float64(window_size))))] = val
            if !NaN_in_slice(buffer) && first_time {
                sum := 0.0
                for _, entry := range buffer {
                    sum += entry
                }
                output[i] = sum / float64(window_size)
                first_time = false
            } else if i > 0 && !math.IsNaN(output[i-1]) && !NaN_in_slice(buffer) {
                output[i] = output[i-1] + (val-old_val)/float64(window_size) // 无循环的解决方案
            } else {
                output[i] = math.NaN()
            }
        }
    } else { // 空输入
        fmt.Println("moving_avg is panicking!")
        panic(fmt.Sprintf("%v", input))
    }
    return output
}

// 返回包含输入移动平均值的列表
// 重新排列控制结构以利用短路求值
func moving_avg_serial4(input []float64, window_size int) []float64 {
    first_time := true
    var output = make([]float64, len(input))
    if len(input) > 0 {
        var buffer = make([]float64, window_size)
        // 初始化缓冲区为 NaN
        for i := range buffer {
            buffer[i] = math.NaN()
        }
        for i := range input {
            //            fmt.Printf("in mvg_avg4: i=%v\n", i)
            old_val := buffer[int((math.Mod(float64(i), float64(window_size))))]
            buffer[int((math.Mod(float64(i), float64(window_size))))] = input[i]
            if first_time && !NaN_in_slice(buffer) {
                sum := 0.0
                for j := range buffer {
                    sum += buffer[j]
                }
                output[i] = sum / float64(window_size)
                first_time = false
            } else if i > 0 && !math.IsNaN(output[i-1]) /* && !NaN_in_slice(buffer)*/ {
                output[i] = output[i-1] + (input[i]-old_val)/float64(window_size) // 无循环的解决方案
            } else {
                output[i] = math.NaN()
            }
        }
    } else { // 空输入
        fmt.Println("moving_avg is panicking!")
        panic(fmt.Sprintf("%v", input))
    }
    return output
}

// 返回包含输入移动平均值的列表
// 将列表拆分为较小的片段以使用 goroutine，但不使用串行版本，即我们仅在开头具有 NaN，因此希望减少一些开销
// 仍然不能扩展（随着大小和 num_goroutines 的增加，性能下降）
func moving_avg_concurrent2(input []float64, window_size, num_goroutines int) []float64 {
    var output = make([]float64, window_size-1, len(input))
    for i := 0; i  0 {
        num_items := len(input) - (window_size - 1)
        var barrier_wg sync.WaitGroup
        n := num_items / num_goroutines
        go_avg := make([][]float64, num_goroutines)
        for i := 0; i  0 {
        num_windows := len(input) - (window_size - 1)
        var output = make([]float64, len(input))
        for i := 0; i 

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