Cool, thanks! Let me know if there are any more core numerical libraries
that you'd like to see to support Spark with optimised natives using a
similar packaging model at netlib-java.
I'm interested in fast random number generation next, and I keep wondering
if anybody would be interested in paying for FPGA or GPU / APU backends for
netlib-java. It would be a *lot* of work but I'd be very interested to talk
to an organisation with such a requirement and I'd be able to do it in less
time than they would internally.
On 10 Feb 2015 04:12, Andrew Ash [via Apache Spark Developers List]
ml-node+s1001551n10546...@n3.nabble.com wrote:
Sam, I see your PR was merged -- many thanks for sending it in and getting
it merged!
In general for future reference, the most effective way to contribute is
outlined on this wiki page:
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
On Mon, Feb 9, 2015 at 1:04 AM, Akhil Das [hidden email]
http:///user/SendEmail.jtp?type=nodenode=10546i=0
wrote:
You can open a Jira issue pointing this PR to get it processed faster.
:)
Thanks
Best Regards
On Sat, Feb 7, 2015 at 7:07 AM, fommil [hidden email]
http:///user/SendEmail.jtp?type=nodenode=10546i=1 wrote:
Hi all,
I'm the author of netlib-java and I noticed that the documentation in
MLlib
was out of date and misleading, so I submitted a pull request on
github
which will hopefully make things easier for everybody to understand
the
benefits of system optimised natives and how to use them :-)
https://github.com/apache/spark/pull/4448
However, it looks like there are a *lot* of outstanding PRs and that
this
is
just a mirror repository.
Will somebody please look at my PR and merge into the canonical source
(and
let me know)?
Best regards,
Sam
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