Github user vrilleup commented on the pull request:
https://github.com/apache/spark/pull/964#issuecomment-48247731
@yangliuyu sorry for the delayed reply. I think there has to be enough
parallelism to fully power Spark. If you have 16 cores, why not get more
executors or assign more cores per executor to allow more tasks running at the
same time? I am not sure about the scheduler delay and GC time. In my test case
with n = 300k, scheduler delay is 0.3s (max 1.0s), and GC takes no time
(occasionally takes <100ms in some tasks). Do you have enough memory to hold
all the singular vectors? n * (6 * k + 4) doubles need to fit in the master
node memory for ARPACK, and additional memory is required to cache the RDD.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
