我正在尝试为我们试图解决的遗传问题建模，并逐步加以解决。我可以从Spark示例成功运行PiAverage示例。该示例将一个圆圈“掷飞镖”（在本例中为10 ^
6），并计算“落在圆圈中”的数量以估算PI

假设我要重复该过程1000次（并行），并对所有这些估计求平均值。我正在尝试寻找最好的方法，似乎会有两个调用要并行化？嵌套通话？有没有办法将地图链接或减少通话数量？我看不到

我想知道以下想法的智慧。我想到了使用累加器跟踪结果估算。jsc是我的SparkContext，单次运行的完整代码在问题末尾，谢谢您的投入！

Accumulator<Double> accum = jsc.accumulator(0.0);

// make a list 1000 long to pass to parallelize (no for loops in Spark, right?)
List<Integer> numberOfEstimates = new ArrayList<Integer>(HOW_MANY_ESTIMATES);

// pass this "dummy list" to parallelize, which then 
// calls a pieceOfPI method to produce each individual estimate  
// accumulating the estimates. PieceOfPI would contain a 
// parallelize call too with the individual test in the code at the end
jsc.parallelize(numberOfEstimates).foreach(accum.add(pieceOfPI(jsc, numList, slices, HOW_MANY_ESTIMATES)));

// get the value of the total of PI estimates and print their average
double totalPi = accum.value();

// output the average of averages
System.out.println("The average of " + HOW_MANY_ESTIMATES + " estimates of Pi is " + totalPi / HOW_MANY_ESTIMATES);

我在SO上看到的矩阵或其他答案似乎并没有给出该特定问题的答案，我进行了几次搜索，但是如果不“并行化并行化”，我将看不到如何做到这一点。那是个坏主意吗？

（是的，我从数学上意识到我可以做更多的估算并有效地获得相同的结果：）试图建立老板想要的结构，再次感谢！

如果没有帮助，我将整个测试程序放在这里，如果没有测试过的累加器。其核心将成为PieceOfPI（）：

import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;

import org.apache.spark.Accumulable;
import org.apache.spark.Accumulator;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.storage.StorageLevel;
import org.apache.spark.SparkConf;
import org.apache.spark.storage.StorageLevel;

public class PiAverage implements Serializable {

public static void main(String[] args) {

    PiAverage pa = new PiAverage();
    pa.go();

}

public void go() {

    // should make a parameter like all these finals should be
    // int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2;
    final int SLICES = 16;

    // how many "darts" are thrown at the circle to get one single Pi estimate
    final int HOW_MANY_DARTS = 1000000;

    // how many "dartboards" to collect to average the Pi estimate, which we hope converges on the real Pi
    final int HOW_MANY_ESTIMATES = 1000;

    SparkConf sparkConf = new SparkConf().setAppName("PiAverage")
        .setMaster("local[4]");

    JavaSparkContext jsc = new JavaSparkContext(sparkConf);

    // setup "dummy" ArrayList of size HOW_MANY_DARTS -- how many darts to throw
    List<Integer> throwsList = new ArrayList<Integer>(HOW_MANY_DARTS);
    for (int i = 0; i < HOW_MANY_DARTS; i++) {
        throwsList.add(i);
    }

    // setup "dummy" ArrayList of size HOW_MANY_ESTIMATES
    List<Integer> numberOfEstimates = new ArrayList<Integer>(HOW_MANY_ESTIMATES);
    for (int i = 0; i < HOW_MANY_ESTIMATES; i++) {
        numberOfEstimates.add(i);
    }

    JavaRDD<Integer> dataSet = jsc.parallelize(throwsList, SLICES);

    long totalPi = dataSet.filter(new Function<Integer, Boolean>() {
        public Boolean call(Integer i) {
            double x = Math.random();
            double y = Math.random();
            if (x * x + y * y < 1) {
                return true;
            } else
                return false;
        }
    }).count();

    System.out.println(
            "The average of " + HOW_MANY_DARTS + " estimates of Pi is " + 4 * totalPi / (double)HOW_MANY_DARTS);

    jsc.stop();
    jsc.close();
}
}