阿卡 - 更多演员表现更差

我正在尝试使用Scala和Akka进行一些并行编程,我是新手.我有一个非常简单的Monte Carlo Pi应用程序(近似于一个圆圈中的pi),我用几种语言构建了它.然而,我在Akka中构建的版本的性能令我感到困惑.

我有一个用纯Scala编写的顺序版本,大约需要400ms才能完成.

与1名工人演员相比,Akka版本需要大约300-350ms,但是当我增加演员的数量时,时间会急剧增加.有4名演员,时间可以在500毫秒之间,一直到1200毫秒或更高.

迭代次数在工作者之间进行划分,所以理想情况下,性能越多越好,目前情况会越来越差.

我的代码是

object MCpi{
  //Declare initial values
  val numWorkers = 2
  val numIterations = 10000000

  //Declare messages that will be sent to actors
  sealed trait PiMessage
  case object Calculate extends PiMessage
  case class Work(iterations: Int) extends PiMessage
  case class Result(value: Int) extends PiMessage
  case class PiApprox(pi: Double, duration: Double)

  //Main method
  def main(args: Array[String]): Unit = {
    val system = ActorSystem("MCpi_System") //Create Akka system
    val master = system.actorOf(Props(new MCpi_Master(numWorkers, numIterations))) //Create Master Actor
    println("Starting Master")

    master ! Calculate //Run calculation
  }
}

//Master
class MCpi_Master(numWorkers: Int, numIterations: Int) extends Actor{

  var pi: Double = _ // Store pi
  var quadSum: Int = _ //the total number of points inside the quadrant
  var numResults: Int = _ //number of results returned
  val startTime: Double = System.currentTimeMillis() //calculation start time

  //Create a group of worker actors
  val workerRouter = context.actorOf(
    Props[MCpi_Worker].withRouter(RoundRobinPool(numWorkers)), name = "workerRouter")
  val listener = context.actorOf(Props[MCpi_Listener], name = "listener")

  def receive = {
    //Tell workers to start the calculation
      //For each worker a message is sent with the number of iterations it is to perform,
      //iterations are split up between the number of workers.
    case Calculate => for(i <- 0 until numWorkers) workerRouter ! Work(numIterations / numWorkers);

      //Receive the results from the workers
        case Result(value) =>
            //Add up the total number of points in the circle from each worker
      quadSum += value
            //Total up the number of results which have been received, this should be 1 for each worker
      numResults += 1

      if(numResults == numWorkers) { //Once all results have been collected
          //Calculate pi
          pi = (4.0 * quadSum) / numIterations
          //Send the results to the listener to output
        listener ! PiApprox(pi, duration = System.currentTimeMillis - startTime)
        context.stop(self)
      }
  }
}
//Worker
class MCpi_Worker extends Actor {
  //Performs the calculation
  def calculatePi(iterations: Int): Int = {

    val r = scala.util.Random // Create random number generator
    var inQuadrant: Int = 0 //Store number of points within circle

    for(i <- 0 to iterations){
      //Generate random point
      val X = r.nextFloat()
      val Y = r.nextFloat()

      //Determine whether or not the point is within the circle
      if(((X * X) + (Y * Y)) < 1.0)
        inQuadrant += 1
    }
    inQuadrant //return the number of points within the circle
  }

  def receive = {
    //Starts the calculation then returns the result
    case Work(iterations) => sender ! Result(calculatePi(iterations))
  }
}

//Listener
class MCpi_Listener extends Actor{ //Recieves and prints the final result
  def receive = {
    case PiApprox(pi, duration) =>
        //Print the results
      println("\n\tPi approximation: \t\t%s\n\tCalculation time: \t%s".format(pi, duration))
        //Print to a CSV file
        val pw: FileWriter = new FileWriter("../../../..//Results/Scala_Results.csv", true)
        pw.append(duration.toString())
        pw.append("\n")
        pw.close()
      context.system.terminate()

  }
}

普通的Scala顺序版本是

object MCpi {
    def main(args: Array[String]): Unit = {
        //Define the number of iterations to perform
        val iterations = args(0).toInt;
        val resultsPath = args(1);

        //Get the current time
        val start = System.currentTimeMillis()


        // Create random number generator
        val r = scala.util.Random
        //Store number of points within circle
        var inQuadrant: Int = 0

        for(i <- 0 to iterations){
            //Generate random point
            val X = r.nextFloat()
            val Y = r.nextFloat()

            //Determine whether or not the point is within the circle
            if(((X * X) + (Y * Y)) < 1.0)
                inQuadrant += 1
        }
        //Calculate pi
        val pi = (4.0 * inQuadrant) / iterations
        //Get the total time
        val time = System.currentTimeMillis() - start
        //Output values
        println("Number of Iterations: " + iterations)
        println("Pi has been calculated as: " + pi)
        println("Total time taken: " + time + " (Milliseconds)")

        //Print to a CSV file
        val pw: FileWriter = new FileWriter(resultsPath + "/Scala_Results.csv", true)
        pw.append(time.toString())
        pw.append("\n")
        pw.close()
    }
}

关于为什么会发生这种情况或如何提高性能的任何建议都将非常受欢迎.

编辑:我要感谢你们所有人的回答,这是我在这个网站上的第一个问题,所有答案都非常有用,我现在有很多东西要看:)

您Random正在使用的实例周围存在同步问题.

更具体地说,这一行

val r = scala.util.Random // Create random number generator

其实没有"创建随机数生成器",但拿起单object是scala.util方便的为您提供.这意味着所有线程都将共享它,并将围绕其种子进行同步(java.util.Random.nextFloat有关详细信息,请参阅代码).

只需将该行更改为

val r = new scala.util.Random // Create random number generator

你应该得到一些并行化加速.正如评论中所述,加速将取决于您的架构等等,但至少它不会因强同步而如此严重偏差.

请注意,java.util它将System.nanoTime用作新创建的种子Random,因此您不必担心随机化问题.