Apache MLlib随机森林的并行训练

I have a Java application that trains a MLlib Random Forest (org.apache.spark.mllib.tree.RandomForest) on a training-set with 200K samples. I've noticed that only one CPU core is utilised during training. Given that a Random Forest is an ensemble of N Decision Trees, one would think that the trees could be trained in parallel, and thus utilising all available cores. Is there a configuration option or API call or anything else that can enable parallel training of the Decision Trees?I have a Java application that trains a MLlib R