[ISSUE-544] improve speed of reduced set models #746
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Reduced Set models don't use numba for enhanced performance. This uses numba njit wrapper to compile the deriviative functions during runtime, and provide considerable performance gain. Since numba currently doesn't fully support jit-ed classes, derivative functions have to move outside the class scope. To improved readability, they were moved to a new file: _dfun_stefanescu_jirsa.py. To test the changes a new unit test module was added: models_benchmark.py. Here, we compute the derivative multiple times within a time window with/without numba and compare the throughput. Around 5x performance gains were observed. We also verify that numba doesn't change derivative values.
Member
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hi thx! can you just use non-zero coupling values, to ensure the coupling expressions inside the numba dfun are also tested? |
Author
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changed to random coupling with d32512d |
…ba dfun are tested
maedoc
reviewed
Apr 7, 2025
Member
|
looks good to me |
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Reduced Set models don't use numba for enhanced performance. This uses numba njit wrapper to compile the deriviative functions during runtime, and provide considerable performance gain.
Since numba currently doesn't fully support jit-ed classes, derivative functions have to move outside the class scope. To improved readability, they were moved to a new file: _dfun_stefanescu_jirsa.py.
To test the changes a new unit test module was added: models_benchmark.py. Here, we compute the derivative multiple times within a time window with/without numba and compare the throughput. Around 5x performance gains were observed. We also verify that numba doesn't change derivative values.