On Thu, 19 Oct 2017, Allin Cottrell wrote:
On Thu, 19 Oct 2017, Stefano wrote:
> re Jack's suggestion of stopwatching my jobs, I try to explain more clearly
> my question: there _are_ big differences in execution time, that is 100%
> sure. It may depend, for unclear reasons, on the (i) the total number of
> jobs or (ii) the number of Gretl jobs or (iii) both. Given that I cannot
> control RAM and CPU use of other useres I cannot measure the coeteris
> paribus effect of changing the number of my simultaneous Gretl jobs. So the
> question is: does anybody know if there are any reasons why running more
> than one Gretl job at a time may affect speed?
You will get a significant slow-down if the total number of threads exceeds
the number of cores on the machine (or the number of supported threads if
this exceeds the number of "real cores" due to hyper-threading).
By default gretl expects to use OpenMP on a multi-core machine. You may find
that it helps to limit the number of threads spawned by each instance of
gretl, as in
OMP_NUM_THREADS=2 gretlcli <jobspec>
or possibly
OPENBLAS_NUM_THREADS=2 gretlcli <jobspec>
if gretl is linked against an OpenBLAS library that uses "raw" pthreads
instead of OpenMP (not recommended, BTW). You could probably set both
environment variables "just in case".
Worst case is this sort of thing: you have a machine with 8 cores and you
launch 4 gretl instances without limiting their thread usage; then each
instance will try to use 8 threads and performance will degrade severely.
In fact, it'd be quite helpful to know exactly what options were used for
compiling gretl on the machine you're running your jobs on, and possibly
also the BLAS version it was linked against.
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Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
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