for yarn-client mode:
SPARK_EXECUTOR_CORES * SPARK_EXECUTOR_INSTANCES = 2(or 3) *
TotalCoresOnYourCluster
for standlone mode:
SPARK_WORKER_INSTANCES * SPARK_WORKER_CORES = 2(or 3) * TotalCoresOnYourCluster
Best Regards,
Yi Tian
tianyi.asiai...@gmail.com
On Sep 28, 2014, at 17:59, myasuka <myas...@live.com> wrote:
> Hi, everyone
> I come across with a problem about increasing the concurency. In a
> program, after shuffle write, each node should fetch 16 pair matrices to do
> matrix multiplication. such as:
>
> *import breeze.linalg.{DenseMatrix => BDM}
>
> pairs.map(t => {
> val b1 = t._2._1.asInstanceOf[BDM[Double]]
> val b2 = t._2._2.asInstanceOf[BDM[Double]]
>
> val c = (b1 * b2).asInstanceOf[BDM[Double]]
>
> (new BlockID(t._1.row, t._1.column), c)
> })*
>
> Each node has 16 cores. However, no matter I set 16 tasks or more on
> each node, the concurrency cannot be higher than 60%, which means not every
> core on the node is computing. Then I check the running log on the WebUI,
> according to the amount of shuffle read and write in every task, I see some
> task do once matrix multiplication, some do twice while some do none.
>
> Thus, I think of using java multi thread to increase the concurrency. I
> wrote a program in scala which calls java multi thread without Spark on a
> single node, by watch the 'top' monitor, I find this program can use CPU up
> to 1500% ( means nearly every core are computing). But I have no idea how to
> use Java multi thread in RDD transformation.
>
> Is there any one can provide some example code to use Java multi thread
> in RDD transformation, or give any idea to increase the concurrency ?
>
> Thanks for all
>
>
>
>
> --
> View this message in context:
> http://apache-spark-developers-list.1001551.n3.nabble.com/How-to-use-multi-thread-in-RDD-map-function-tp8583.html
> Sent from the Apache Spark Developers List mailing list archive at Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org
> For additional commands, e-mail: dev-h...@spark.apache.org
>
