RStudio Server - UI Script Running vs. Local Jobs Script Performance (and Environments)

I am observing significant performance differences and memory consumption between running a script live as compared to submitting it as a local job to the community edition of RStudio Server.


  • Running Ubuntu 18.04 LTS with RStudio Server (Version 1.2.1335). Server has 64GB of RAM and 16 cores.
  • I have a long running optimization script using the library(GA) (genetic algorithms) library which makes use of the library(doParallel) to provide parallelization across the cores.


  • When I run the script via the console or user interface, the process generally takes 1.25 hours and consumes 20% of the 64GB of RAM for the duration. Average processor utilization is 80% of the 16 cores for the duration.
  • When I submit the script as a job I am observing different behavior. The memory usage spikes to nearly 100% and then encroaches on swap. Following that, processor utilization stays around 1-2% and then every 5 minutes jumps to about 50% for a few seconds. I let the script run for about 2 hours before stopping.

Other Facts:

  • Both scenarios above have no other user initiated processes running on the server or during the job submission.
  • The script is self-contained, sourcing other required R scripts as well as RDS files as needed from the working directory.

Research to Date:

  • I've reviewed the RStudio Server Pro manual and found the rserver.conf and rsession.conf options. They are both currently blank.
  • I have a theory the inconsistent behavior may either have to do with:
    • The R environment and 'copy job results values', however have not found a resolution yet
    • The GA library loading in the job created environment and recognizing the available server resources available (i.e. # of cores). This would not explain the high memory usage though.

Any help or references to overlooked research areas is greatly appreciated. Thank you!

Is there any chance you might be able to provide a reproducible example, so we can try and see the same behavior on our own machines?