Hi Xiangrui,
Yes I am using Spark v0.9 and am not running it in local mode.
I did the memory setting using "export SPARK_MEM=4G" before starting the Spark
instance.
Also previously, I was starting it with -c 1 but changed it to -c 12 since
it is a 12 core machine. It did bring down the time taken to less than 200
seconds from over 700 seconds.
I am not sure how to repartition the data to match the CPU cores. How do I
do it?
Thank you.
Ravi
On Thu, Jul 17, 2014 at 3:17 PM, Xiangrui Meng <men...@gmail.com> wrote:
> Is it v0.9? Did you run in local mode? Try to set --driver-memory 4g
> and repartition your data to match number of CPU cores such that the
> data is evenly distributed. You need 1m * 50 * 8 ~ 400MB to storage
> the data. Make sure there are enough memory for caching. -Xiangrui
>
> On Thu, Jul 17, 2014 at 1:48 AM, Ravishankar Rajagopalan
> <viora...@gmail.com> wrote:
> > I am trying to use MLlib for K-Means clustering on a data set with 1
> million
> > rows and 50 columns (all columns have double values) which is on HDFS
> (raw
> > txt file is 28 MB)
> >
> > I initially tried the following:
> >
> > val data3 = sc.textFile("hdfs://...inputData.txt")
> > val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
> > val numIterations = 10
> > val numClusters = 200
> > val clusters = KMeans.train(parsedData3, numClusters, numIterations)
> >
> > This took me nearly 850 seconds.
> >
> > I tried using persist with MEMORY_ONLY option hoping that this would
> > significantly speed up the algorithm:
> >
> > val data3 = sc.textFile("hdfs://...inputData.txt")
> > val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
> > parsedData3.persist(MEMORY_ONLY)
> > val numIterations = 10
> > val numClusters = 200
> > val clusters = KMeans.train(parsedData3, numClusters, numIterations)
> >
> > This resulted in only a marginal improvement and took around 720 seconds.
> >
> > Is there any other way to speed up the algorithm further?
> >
> > Thank you.
> >
> > Regards,
> > Ravi
>
