kotlin coroutines - use main thread in run blocking

I tested the solution with Java 8 parallel streams:

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

I found the CPU utilization to be reliably on 100%. For reference, I used this computation job:

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
    }
}

Note that its duration varies randomly from zero up to the time it takes to perform 100,000,000 FP multiplications.

Out of curiosity I also studied the code you added to your question as the solution that works for you. I found a number of issues with it, such as:

• accumulating all the results into a list instead of processing them as they become available
• closing the result channel immediately after submitting the last job instead of waiting for all the results

I wrote some code of my own and added code to benchmark the Stream API one-liner against it. Here it is:

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")
}

Here's my typical result:

Parallel Stream took 396.85 ms
Channels took 398.1 ms

The results are similar, but one line of code still beats 50 lines of code :)