Ni! Hi Phillip, That's a feature of OpenMP controlled by an environment variable: OMP_NUM_THREADS So you can, for example export OMP_NUM_THREADS=4 before running your code. .~´ Le mercredi 08 février 2017 à 10:09 -0700, P-M a écrit :
I am trying to obtain the correlation histogram for a graph of mine following the example given in the manual. I run:
g = gt.load_graph('graph.gt') gt.remove_parallel_edges(g) h=gt.corr_hist(g,'out','out')
My graph is relatively large at 12,238,931 vertices and 24,884,365 edges. My problem is that as soon as I start the code it runs on 20 processes and happily chomps through 252 GB of RAM before starting to spill over into the swap making my machine incredibly slow.
I presume the RAM usage is linked to the parallel processing so presumably could be tackled if I ran it using fewer processes. Is there any way of reducing the RAM usage? Or would I need to implement the routine manually to achieve this?
Best wishes,
Philipp
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