Compute Grid Tip of the Month - May 2017

April 26, 2017

Parallel/Multicore Processing

Using multiple cores (CPUs) to analyze data is an efficient way to get more work done in less time. But this is true only under certain circumstances. By default, R, Python, and MATLAB can only use one core even on a multicore (multiCPU) machine, unless you specifically program them to use more. Stata, on the other hand, has been parallelized, so many of its functions can use more than one core, but only to a maximum of 75% efficiency overall. To get the most efficiency, its best to run your 'do' files in batch; if using the interactive GUI, Stata spends more time sitting idle than processing your data, so your parallelization efficiency drops significantly. We counsel you to use Stata-SE for this very reason.

If you are curious about parallel processing on our or any compute grid, or want to know how you can take advantage of more cores in your work, please contact RCS.