Thank you for your reply,
Actually, we have already used this parameter. Our cluster is a
standalone cluster with 16 nodes, every node has 16 cores. We have 256 pairs
matrices along with 256 tasks , when we set --total-executor-cores as 64,
each node can launch 4 tasks simultaneously, each task can do once matrix
multiplication, CPU usage is nearly 25%. If we set --total-executor-cores as
128, each node can launch 8 tasks simultaneously, but not every task do once
matrix multiplication, CPU usage is nearly 35%. Then if we set
--total-executor-cores as 256, each node can launch 16 tasks simultaneously,
but still not every task do once matrix multiplication, some do none some do
twice, CPU usage is nearly 50%.
If we can increase the concurency to increase the CPU usage, thus less
running time we will cost.
Hope for any solution!
Qin Wei wrote
> in the options of spark-submit, there are two options which may be helpful
> to your problem, they are "--total-executor-cores NUM"(standalone and
> mesos only), "--executor-cores"(yarn only)
>
>
> qinwei
> From: myasukaDate: 2014-09-28 11:44To: userSubject: How to use multi
> thread in RDD map function ?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
>
>
>
>
>
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