Hi everyone!
I realized today that profiling PyNEST is easier than I thought. In iPython, you can just run
run -p -s cumulative -D srn.prof ../src/pynest/examples/store_restore_network.py
which will run the script, present a summary sorted by cumulative time and write binary profiling data (pstats format) to file srn.prof.
Then run (gprof2dot available from, e.g., PiPy)
gprof2dot -f pstats -o srn.dot srn.prof
and finally
dot -Tpdf srn.dot
Both tools have lot's of options. In my case (an older version of the script above, currently under review in #1919, not yet in master), the attached PDF resulted, showing that getting connection properties indeed takes a lot of time. Note that the graph only resolves time spent in Python code, time spent in C++ code is hiding behind "run()".
Below some more timing results from a network of 1000 neurons with 100,000 connections:
In [16]: %time c = nest.GetConnections() CPU times: user 66.5 ms, sys: 8.09 ms, total: 74.6 ms Wall time: 75.7 ms
In [17]: %time c = nest.GetConnections().weight CPU times: user 869 ms, sys: 75.3 ms, total: 944 ms Wall time: 955 ms
In [18]: %time c = nest.GetConnections().get("weight", output="pandas") CPU times: user 1.69 s, sys: 186 ms, total: 1.88 s Wall time: 1.9 s
Clearly, GetConnections() is quite fast, while reading out the weights costs. What maybe surprised me most is that turning the data as a Pandas DataFrame costs a whole second extra—I wonder if we do something suboptimal here.
Best, Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser Head, Department of Data Science
Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560 Email hans.ekkehard.plesser@nmbu.nomailto:hans.ekkehard.plesser@nmbu.no Home http://arken.nmbu.no/~plesser
Hi Hans, Is the Python script available still on today's master branch? I don't seem to locate it. Itaru.
On Thu, May 6, 2021 at 6:41 PM Hans Ekkehard Plesser hans.ekkehard.plesser@nmbu.no wrote:
Hi everyone!
I realized today that profiling PyNEST is easier than I thought. In iPython, you can just run
run -p -s cumulative -D srn.prof ../src/pynest/examples/store_restore_network.py
which will run the script, present a summary sorted by cumulative time and write binary profiling data (pstats format) to file srn.prof.
Then run (gprof2dot available from, e.g., PiPy)
gprof2dot -f pstats -o srn.dot srn.prof
and finally
dot -Tpdf srn.dotBoth tools have lot's of options. In my case (an older version of the script above, currently under review in #1919, not yet in master), the attached PDF resulted, showing that getting connection properties indeed takes a lot of time. Note that the graph only resolves time spent in Python code, time spent in C++ code is hiding behind "run()".
Below some more timing results from a network of 1000 neurons with 100,000 connections:
In [16]: %time c = nest.GetConnections()
CPU times: user 66.5 ms, sys: 8.09 ms, total: 74.6 ms
Wall time: 75.7 ms
In [17]: %time c = nest.GetConnections().weight
CPU times: user 869 ms, sys: 75.3 ms, total: 944 ms
Wall time: 955 ms
In [18]: %time c = nest.GetConnections().get("weight", output="pandas")
CPU times: user 1.69 s, sys: 186 ms, total: 1.88 s
Wall time: 1.9 s
Clearly, GetConnections() is quite fast, while reading out the weights costs. What maybe surprised me most is that turning the data as a Pandas DataFrame costs a whole second extra—I wonder if we do something suboptimal here.
Best,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Head, Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser@nmbu.no
Home http://arken.nmbu.no/~plesser
NEST Users mailing list -- users@nest-simulator.org To unsubscribe send an email to users-leave@nest-simulator.org
Hi Itaru,
The script is in the PR #1919 (https://github.com/nest/nest-simulator/pull/1919).
I noticed I forgot to attach the profiling plot to my previous mail, here it comes.
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser Head, Department of Data Science
Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560 Email hans.ekkehard.plesser@nmbu.no Home http://arken.nmbu.no/~plesser
On 07/05/2021, 08:09, "Itaru Kitayama" itaru.kitayama@gmail.com wrote:
Hi Hans, Is the Python script available still on today's master branch? I don't seem to locate it. Itaru.
On Thu, May 6, 2021 at 6:41 PM Hans Ekkehard Plesser hans.ekkehard.plesser@nmbu.no wrote: > > > > Hi everyone! > > > > I realized today that profiling PyNEST is easier than I thought. In iPython, you can just run > > > > run -p -s cumulative -D srn.prof ../src/pynest/examples/store_restore_network.py > > > > which will run the script, present a summary sorted by cumulative time and write binary profiling data (pstats format) to file srn.prof. > > > > Then run (gprof2dot available from, e.g., PiPy) > > > > gprof2dot -f pstats -o srn.dot srn.prof > > > > and finally > > > > dot -Tpdf srn.dot > > > > Both tools have lot's of options. In my case (an older version of the script above, currently under review in #1919, not yet in master), the attached PDF resulted, showing that getting connection properties indeed takes a lot of time. Note that the graph only resolves time spent in Python code, time spent in C++ code is hiding behind "run()". > > > > Below some more timing results from a network of 1000 neurons with 100,000 connections: > > > > In [16]: %time c = nest.GetConnections() > > CPU times: user 66.5 ms, sys: 8.09 ms, total: 74.6 ms > > Wall time: 75.7 ms > > > > In [17]: %time c = nest.GetConnections().weight > > CPU times: user 869 ms, sys: 75.3 ms, total: 944 ms > > Wall time: 955 ms > > > > In [18]: %time c = nest.GetConnections().get("weight", output="pandas") > > CPU times: user 1.69 s, sys: 186 ms, total: 1.88 s > > Wall time: 1.9 s > > > > Clearly, GetConnections() is quite fast, while reading out the weights costs. What maybe surprised me most is that turning the data as a Pandas DataFrame costs a whole second extra—I wonder if we do something suboptimal here. > > > > Best, > > Hans Ekkehard > > > > -- > > > > Prof. Dr. Hans Ekkehard Plesser > > Head, Department of Data Science > > > > Faculty of Science and Technology > > Norwegian University of Life Sciences > > PO Box 5003, 1432 Aas, Norway > > > > Phone +47 6723 1560 > > Email hans.ekkehard.plesser@nmbu.no > > Home http://arken.nmbu.no/~plesser > > > > > > _______________________________________________ > NEST Users mailing list -- users@nest-simulator.org > To unsubscribe send an email to users-leave@nest-simulator.org _______________________________________________ NEST Users mailing list -- users@nest-simulator.org To unsubscribe send an email to users-leave@nest-simulator.org
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Hans Ekkehard Plesser -
Itaru Kitayama