The decrease in running time from N=6 to N=7 makes some sense to me; that should be when tree aggregation kicks in. I'd call it an improvement to run in the same ~13sec increasing from N=6 to N=9.
"Does this mean that for 5 nodes with treeaggreate of depth 1 it will take 5*3.1~15.5 seconds?" --> I would guess so since all of that will be aggregated on the driver, but I don't know enough about Spark's shuffling/networking to say for sure. Others may be able to help more. Your numbers do make me wonder if we should examine the structure of the tree aggregation more carefully and see if we can improve it. https://issues.apache.org/jira/browse/SPARK-11168 Joseph On Thu, Oct 15, 2015 at 7:01 PM, Ulanov, Alexander <alexander.ula...@hpe.com > wrote: > Hi Joseph, > > > > There seems to be no improvement if I run it with more partitions or > bigger depth: > > N = 6 Avg time: 13.491579108666668 > > N = 7 Avg time: 8.929480508 > > N = 8 Avg time: 14.507123471999998 > > N= 9 Avg time: 13.854871645333333 > > > > Depth = 3 > > N=2 Avg time: 8.853895346333333 > > N=5 Avg time: 15.991574924666667 > > > > I also measured the bandwidth of my network with iperf. It shows > 247Mbit/s. So the transfer of 12M array of double message should take 64 * > 12M/247M~3.1s. Does this mean that for 5 nodes with treeaggreate of depth 1 > it will take 5*3.1~15.5 seconds? > > > > Best regards, Alexander > > *From:* Joseph Bradley [mailto:jos...@databricks.com] > *Sent:* Wednesday, October 14, 2015 11:35 PM > *To:* Ulanov, Alexander > *Cc:* dev@spark.apache.org > *Subject:* Re: Gradient Descent with large model size > > > > For those numbers of partitions, I don't think you'll actually use tree > aggregation. The number of partitions needs to be over a certain threshold > (>= 7) before treeAggregate really operates on a tree structure: > > > https://github.com/apache/spark/blob/9808052b5adfed7dafd6c1b3971b998e45b2799a/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L1100 > > > > Do you see a slower increase in running time with more partitions? For 5 > partitions, do you find things improve if you tell treeAggregate to use > depth > 2? > > > > Joseph > > > > On Wed, Oct 14, 2015 at 1:18 PM, Ulanov, Alexander < > alexander.ula...@hpe.com> wrote: > > Dear Spark developers, > > > > I have noticed that Gradient Descent is Spark MLlib takes long time if the > model is large. It is implemented with TreeAggregate. I’ve extracted the > code from GradientDescent.scala to perform the benchmark. It allocates the > Array of a given size and the aggregates it: > > > > val dataSize = 12000000 > > val n = 5 > > val maxIterations = 3 > > val rdd = sc.parallelize(0 until n, n).cache() > > rdd.count() > > var avgTime = 0.0 > > for (i <- 1 to maxIterations) { > > val start = System.nanoTime() > > val result = rdd.treeAggregate((new Array[Double](dataSize), 0.0, 0L))( > > seqOp = (c, v) => { > > // c: (grad, loss, count) > > val l = 0.0 > > (c._1, c._2 + l, c._3 + 1) > > }, > > combOp = (c1, c2) => { > > // c: (grad, loss, count) > > (c1._1, c1._2 + c2._2, c1._3 + c2._3) > > }) > > avgTime += (System.nanoTime() - start) / 1e9 > > assert(result._1.length == dataSize) > > } > > println("Avg time: " + avgTime / maxIterations) > > > > If I run on my cluster of 1 master and 5 workers, I get the following > results (given the array size = 12M): > > n = 1: Avg time: 4.555709667333333 > > n = 2 Avg time: 7.059724584666667 > > n = 3 Avg time: 9.937117377666667 > > n = 4 Avg time: 12.687526233 > > n = 5 Avg time: 12.939526129666667 > > > > Could you explain why the time becomes so big? The data transfer of 12M > array of double should take ~ 1 second in 1Gbit network. There might be > other overheads, however not that big as I observe. > > Best regards, Alexander > > >
