Thanks Sam for suggestion! I should try doing this. Now I suppose that
netlib-java linked with cuBlas during the execution time does fall back to
cblas library in my system, which is atlas. If I remove atlas, netlib (linked
with cublas) fails with the message "undefined symbol: cblas_dgemm".
*TL;DR*: Hit y on any file page on GitHub to update the URL to a permanent
link.
Many of you probably already know this. Here’s a handy tip for the rest.
So you’re on Github and you want to link to a file in an email, PR, or JIRA
report. Or better yet, you want to link to some specific lines in a
I might take a look at that pr if we get around to doing some perf testing
of Spark on various resource managers.
2015년 3월 2일 (월) 오후 12:22, Shivaram Venkataraman 님이
작성:
FWIW there is a PR open to add support for Hadoop 2.4 to spark-ec2 scripts
> at https://github.com/mesos/spark-ec2/pull/77 -- Bu
Hi Xiangrui,
Thanks for the link, I am currently trying to use nvblas. It seems that netlib
wrappers are implemented with C-BLAS interface and nvblas does not have c-blas.
I wonder how it is going to work. I'll keep you updated.
Alexander
-Original Message-
From: Xiangrui Meng [mailto:
On Fri, Feb 27, 2015 at 12:33 PM, Sam Halliday wrote:
> Also, check the JNILoader output.
>
> Remember, for netlib-java to use your system libblas all you need to do is
> setup libblas.so.3 like any native application would expect.
>
> I haven't ever used the cublas "real BLAS" implementation, so
FWIW there is a PR open to add support for Hadoop 2.4 to spark-ec2 scripts
at https://github.com/mesos/spark-ec2/pull/77 -- But it hasnt' received
much review or testing to be merged.
Thanks
Shivaram
On Sun, Mar 1, 2015 at 11:49 PM, Sean Owen wrote:
> I agree with that. My anecdotal impression
You can use the parallelize method:
val data = List(
Row(1, 5, "vlr1", 10.5),
Row(2, 1, "vl3", 0.1),
Row(3, 8, "vl3", 10.0),
Row(4, 1, "vl4", 1.0))
val rdd = sc.parallelize(data)
Here I'm using a list of Rows, but you could use it with a list of
other kind of object, like this:
val x =
Dears,
I'm just curious about the complexity of the query optimizer. Can the
optimizer evaluates what after the SQL? maybe it's a stupid question ,, but
here is an example to show the case:
>From the Spark SQL example:
val teenagers = sqlContext.sql("SELECT * FROM people WHERE age >= 13 AND age
<
