Kotlin协程-在运行阻塞中使用主线程

Question

我正在尝试执行以下代码：

 val jobs = listOf(...)
 return runBlocking(CommonPool) {
    val executed = jobs.map {
        async { it.execute() }
    }.toTypedArray()
    awaitAll(*executed)
 }

其中jobs是一些Supplier的列表-在同步世界中，这应该只创建例如int列表。 一切正常，但问题是未使用主线程。 YourKit的Bellow屏幕截图：

所以，问题是-我也如何利用主线程？

我想这里是runBlocking的问题，但是还有其他方法可以得到相同的结果吗？使用Java并行流，它看起来要好得多，但是主线程仍然没有被完全利用（任务是完全独立的）。

更新

好的，也许我告诉你的东西太少了。 在观看Vankant Subramaniam的演讲：https://youtu.be/0hQvWIdwnw4之后的一段时间，我提出了问题。 我需要最高的性能，没有IO，没有Ui等。只有计算。只有请求，我需要使用所有可用资源。

我认为必须将paralleizm设置为线程数+ 1，但是我认为这很愚蠢。

Answer 1

仅仅因为在该显式线程上没有运行任何工作，并不意味着该设备不在同一核心上运行其他线程。

让MainThread处于空闲状态实际上更好，这将使您的UI更具响应性。

Answer 2

首先，我想领会使用主线程通常没有任何实际用途。

如果您的应用程序是完全异步的，那么您将只有一个（主）线程被阻塞。该线程的确消耗了一些内存，并给调度程序增加了一些额外的压力，但是对性能的增加影响可以忽略不计，甚至无法衡量。

在实际的Java世界中，几乎不可能在JVM中拥有固定数量的线程。有系统线程（gc），有nio线程，等等。

一个线程没有影响。只要您的应用程序中的线程数不受负载增加的影响就可以了。

---

回到原始问题。

我认为在这种并行处理任务中没有一种利用主线程的简洁方法。

例如，您可以执行以下操作：

data class Job(val res: Int) {
    fun execute(): Int {
        Thread.sleep(100)
        println("execute $res in ${Thread.currentThread().name}")
        return res
    }
}

fun main() {
    val jobs = (1..100).map { Job(it) }
    val resultChannel = Channel<Int>(Channel.UNLIMITED)
    val mainInputChannel = Channel<Job>()

    val workers = (1..10).map {
        actor<Job>(CommonPool) {
            for (j in channel) {
                resultChannel.send(j.execute())
            }
        }
    }

    val res: Deferred<List<Int>> = async(CommonPool) {
        val allChannels = (listOf(mainInputChannel) + workers)

        jobs.forEach { job ->
            select {
                allChannels.forEach {
                    it.onSend(job) {}
                }
            }
        }

        allChannels.forEach { it.close() }
        (1..jobs.size).map { resultChannel.receive() }
    }

    runBlocking {
        for (j in mainInputChannel) {
            resultChannel.send(j.execute())
        }
    }

    runBlocking {
        res.await().forEach { println(it) }
    }
}

基本上是一种简单的生产者/使用者实现，其中主线程充当使用者之一。但这会导致很多样板。

输出：

execute 1 in main @coroutine#12
execute 5 in ForkJoinPool.commonPool-worker-1 @coroutine#4
execute 6 in ForkJoinPool.commonPool-worker-2 @coroutine#5
execute 7 in ForkJoinPool.commonPool-worker-7 @coroutine#6
execute 2 in ForkJoinPool.commonPool-worker-6 @coroutine#1
execute 8 in ForkJoinPool.commonPool-worker-4 @coroutine#7
execute 4 in ForkJoinPool.commonPool-worker-5 @coroutine#3
execute 3 in ForkJoinPool.commonPool-worker-3 @coroutine#2
execute 12 in main @coroutine#12
execute 10 in ForkJoinPool.commonPool-worker-7 @coroutine#9
execute 15 in ForkJoinPool.commonPool-worker-5 @coroutine#6
execute 11 in ForkJoinPool.commonPool-worker-3 @coroutine#10
execute 16 in ForkJoinPool.commonPool-worker-6 @coroutine#1
execute 9 in ForkJoinPool.commonPool-worker-1 @coroutine#8
execute 14 in ForkJoinPool.commonPool-worker-4 @coroutine#5
execute 13 in ForkJoinPool.commonPool-worker-2 @coroutine#4
execute 20 in main @coroutine#12
execute 17 in ForkJoinPool.commonPool-worker-5 @coroutine#2
execute 18 in ForkJoinPool.commonPool-worker-3 @coroutine#3
execute 24 in ForkJoinPool.commonPool-worker-1 @coroutine#6
execute 23 in ForkJoinPool.commonPool-worker-4 @coroutine#5
execute 22 in ForkJoinPool.commonPool-worker-2 @coroutine#4
execute 19 in ForkJoinPool.commonPool-worker-7 @coroutine#7
execute 21 in ForkJoinPool.commonPool-worker-6 @coroutine#1
execute 25 in ForkJoinPool.commonPool-worker-5 @coroutine#8
execute 28 in main @coroutine#12
execute 29 in ForkJoinPool.commonPool-worker-2 @coroutine#2
execute 30 in ForkJoinPool.commonPool-worker-7 @coroutine#3
execute 27 in ForkJoinPool.commonPool-worker-4 @coroutine#10
execute 26 in ForkJoinPool.commonPool-worker-1 @coroutine#9
execute 32 in ForkJoinPool.commonPool-worker-3 @coroutine#4
execute 31 in ForkJoinPool.commonPool-worker-6 @coroutine#1
execute 36 in ForkJoinPool.commonPool-worker-5 @coroutine#8
execute 35 in ForkJoinPool.commonPool-worker-4 @coroutine#7
execute 33 in ForkJoinPool.commonPool-worker-2 @coroutine#5
execute 38 in ForkJoinPool.commonPool-worker-3 @coroutine#2
execute 37 in main @coroutine#12
execute 34 in ForkJoinPool.commonPool-worker-7 @coroutine#6
execute 39 in ForkJoinPool.commonPool-worker-6 @coroutine#3
execute 40 in ForkJoinPool.commonPool-worker-1 @coroutine#1
execute 44 in ForkJoinPool.commonPool-worker-5 @coroutine#8
execute 41 in ForkJoinPool.commonPool-worker-4 @coroutine#4
execute 46 in ForkJoinPool.commonPool-worker-1 @coroutine#2
execute 47 in ForkJoinPool.commonPool-worker-6 @coroutine#1
execute 45 in main @coroutine#12
execute 42 in ForkJoinPool.commonPool-worker-2 @coroutine#9
execute 43 in ForkJoinPool.commonPool-worker-7 @coroutine#10
execute 48 in ForkJoinPool.commonPool-worker-3 @coroutine#3
execute 52 in ForkJoinPool.commonPool-worker-5 @coroutine#8
execute 49 in ForkJoinPool.commonPool-worker-1 @coroutine#5
execute 54 in ForkJoinPool.commonPool-worker-2 @coroutine#1
execute 53 in main @coroutine#12
execute 50 in ForkJoinPool.commonPool-worker-4 @coroutine#6
execute 51 in ForkJoinPool.commonPool-worker-6 @coroutine#7
execute 56 in ForkJoinPool.commonPool-worker-3 @coroutine#3
execute 55 in ForkJoinPool.commonPool-worker-7 @coroutine#2
execute 60 in ForkJoinPool.commonPool-worker-5 @coroutine#8
execute 61 in ForkJoinPool.commonPool-worker-1 @coroutine#5
execute 57 in ForkJoinPool.commonPool-worker-4 @coroutine#4
execute 59 in ForkJoinPool.commonPool-worker-3 @coroutine#10
execute 64 in ForkJoinPool.commonPool-worker-7 @coroutine#2
execute 58 in ForkJoinPool.commonPool-worker-6 @coroutine#9
execute 62 in ForkJoinPool.commonPool-worker-2 @coroutine#1
execute 63 in main @coroutine#12
execute 68 in ForkJoinPool.commonPool-worker-5 @coroutine#8
execute 65 in ForkJoinPool.commonPool-worker-1 @coroutine#3
execute 66 in ForkJoinPool.commonPool-worker-4 @coroutine#6
execute 67 in ForkJoinPool.commonPool-worker-7 @coroutine#7
execute 69 in ForkJoinPool.commonPool-worker-6 @coroutine#4
execute 70 in ForkJoinPool.commonPool-worker-3 @coroutine#2
execute 74 in ForkJoinPool.commonPool-worker-2 @coroutine#1
execute 75 in main @coroutine#12
execute 71 in ForkJoinPool.commonPool-worker-5 @coroutine#5
execute 76 in ForkJoinPool.commonPool-worker-7 @coroutine#3
execute 73 in ForkJoinPool.commonPool-worker-6 @coroutine#10
execute 78 in ForkJoinPool.commonPool-worker-4 @coroutine#6
execute 72 in ForkJoinPool.commonPool-worker-1 @coroutine#9
execute 77 in ForkJoinPool.commonPool-worker-3 @coroutine#8
execute 79 in ForkJoinPool.commonPool-worker-2 @coroutine#1
execute 83 in main @coroutine#12
execute 84 in ForkJoinPool.commonPool-worker-4 @coroutine#3
execute 85 in ForkJoinPool.commonPool-worker-5 @coroutine#5
execute 82 in ForkJoinPool.commonPool-worker-1 @coroutine#7
execute 81 in ForkJoinPool.commonPool-worker-6 @coroutine#4
execute 80 in ForkJoinPool.commonPool-worker-7 @coroutine#2
execute 89 in ForkJoinPool.commonPool-worker-3 @coroutine#8
execute 90 in ForkJoinPool.commonPool-worker-2 @coroutine#1
execute 91 in main @coroutine#12
execute 86 in ForkJoinPool.commonPool-worker-5 @coroutine#6
execute 88 in ForkJoinPool.commonPool-worker-6 @coroutine#10
execute 87 in ForkJoinPool.commonPool-worker-1 @coroutine#9
execute 92 in ForkJoinPool.commonPool-worker-7 @coroutine#2
execute 93 in ForkJoinPool.commonPool-worker-4 @coroutine#3
execute 99 in main @coroutine#12
execute 97 in ForkJoinPool.commonPool-worker-3 @coroutine#8
execute 98 in ForkJoinPool.commonPool-worker-2 @coroutine#1
execute 95 in ForkJoinPool.commonPool-worker-1 @coroutine#5
execute 100 in ForkJoinPool.commonPool-worker-4 @coroutine#6
execute 94 in ForkJoinPool.commonPool-worker-5 @coroutine#4
execute 96 in ForkJoinPool.commonPool-worker-7 @coroutine#7
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Answer 3

我使用Java 8并行流测试了该解决方案：

jobs.parallelStream().forEach { it.execute() }

我发现CPU利用率可靠地达到了100％。作为参考，我使用了此计算作业：

class MyJob {
    fun execute(): Double {
        val rnd = ThreadLocalRandom.current()
        var d = 1.0
        (1..rnd.nextInt(1_000_000)).forEach { _ ->
            d *= 1 + rnd.nextDouble(0.0000001)
        }
        return d
    }
}

请注意，其持续时间从零到执行100,000,000 FP乘法所需的时间随机变化。

出于好奇，我还研究了您添加到问题中的代码，作为适合您的解决方案。我发现它有很多问题，例如：

• 将所有结果累积到一个列表中，而不是在可用时对其进行处理
• 提交最后一份工作后立即关闭结果渠道，而不是等待所有结果

我编写了一些自己的代码，并添加了一些代码以对Stream API进行单行基准测试。在这里：

const val NUM_JOBS = 1000
val jobs = (0 until NUM_JOBS).map { MyJob() }


fun parallelStream(): Double =
        jobs.parallelStream().map { it.execute() }.collect(summingDouble { it })

fun channels(): Double {
    val resultChannel = Channel<Double>(UNLIMITED)

    val mainComputeChannel = Channel<MyJob>()
    val poolComputeChannels = (1..commonPool().parallelism).map { _ ->
        GlobalScope.actor<MyJob>(Dispatchers.Default) {
            for (job in channel) {
                job.execute().also { resultChannel.send(it) }
            }
        }
    }
    val allComputeChannels = poolComputeChannels + mainComputeChannel

    // Launch a coroutine that submits the jobs
    GlobalScope.launch {
        jobs.forEach { job ->
            select {
                allComputeChannels.forEach { chan ->
                    chan.onSend(job) {}
                }
            }
        }
    }

    // Run the main loop which takes turns between running a job
    // submitted to the main thread channel and receiving a result
    return runBlocking {
        var completedCount = 0
        var sum = 0.0
        while (completedCount < NUM_JOBS) {
            select<Unit> {
                mainComputeChannel.onReceive { job ->
                    job.execute().also { resultChannel.send(it) }
                }
                resultChannel.onReceive { result ->
                    sum += result
                    completedCount++
                }
            }
        }
        sum
    }
}

fun main(args: Array<String>) {
    measure("Parallel Stream", ::parallelStream)
    measure("Channels", ::channels)
    measure("Parallel Stream", ::parallelStream)
    measure("Channels", ::channels)
}

fun measure(task: String, measuredCode: () -> Double) {
    val block = { print(measuredCode().toString().substringBefore('.')) }
    println("Warming up $task")
    (1..20).forEach { _ -> block() }
    println("\nMeasuring $task")
    val average = (1..20).map { measureTimeMillis(block) }.average()
    println("\n$task took $average ms")
}

这是我的典型结果：

Parallel Stream took 396.85 ms
Channels took 398.1 ms

结果相似，但是一行代码仍然胜过50行代码：）

Answer 4

没有使用DefaultDispatcher的任何参数的async（），它将从父级获取缓冲池，因此所有在CommonPool中执行的异步调用。如果要让不同的线程集运行代码，请创建自己的池。 虽然通常不使用主线程进行计算是一种好习惯，但是要取决于您的用例。