利用spark算法对人口普查收入数据集进行随机森林分类。我的工人（w1和w2）有一个cpu（4核）。当我将spark配置为仅使用w1作为worker时，构建模型大约需要12秒。当我将spark配置为同时使用w1和w2作为worker时，再次需要12秒来构建模型。我可以看到，在运行代码时，这两个worker的cpu使用率都很高。不过，我希望达到一个较低的执行时间！。当我使用更大的数据集时，我可以达到更少的执行时间。
下面是我的代码片段：

import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.util.MLUtils

// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "data/Census-income.libsvm")
// Split the data into training and test sets (30% held out for testing)
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))

// Train a RandomForest model.
// Empty categoricalFeaturesInfo indicates all features are continuous.
val numClasses = 2
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 3 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "gini"
val maxDepth = 4
val maxBins = 32

val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)

// Evaluate model on test instances and compute test error
val labelAndPreds = testData.map { point =>
  val prediction = model.predict(point.features)
(point.label, prediction)
}
val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
println(s"Test Error = $testErr")

有什么问题？如有任何意见，我们将不胜感激。