The big question is whether each worker or thread uses parallel
processing itself, or whether it uses resources like cache in which
case 20 threads fighting over the cache would slow you down
substantially. If your simulations use operations implemented in BLAS
or LAPACK, be aware that some R insta
On 2020-09-03 13:44 -0400, Leslie Rutkowski wrote:
> Hi all,
>
> I'm working on a large simulation and
> I'm using the doParallel package to
> parallelize my work. I have 20 cores
> on my machine and would like to
> preserve some for day-to-day
> activities - word processing, sending
> email
Do you have 20 actual cores or 10 cores/20 threads? detectCores() doesn't
usually know the difference but the CPU may be too busy accessing memory to let
that last thread get any useful work done. I often find that allocating real
cores is more practical than thinking in terms of thread so try a
Hi Leslie and all.
You may want to investigate using SparklyR on a cloud environment like
AWS, where you have more packages that are designed to work on cluster
computing environments and you have more control over those types of
parallel operations.
V/r,
Tom W.
Quoting Leslie Rutkows
Hi all,
I'm working on a large simulation and I'm using the doParallel package to
parallelize my work. I have 20 cores on my machine and would like to
preserve some for day-to-day activities - word processing, sending emails,
etc.
I started by saving 1 core and it was clear that *everything* was
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