Comments (2)
I added DFT benchmarks in #103. Comparing different solvers in a benchmark is not useful imo, because it gives a very limited insight into the capabilities of the solver.
Do you have a specific thought on how to identify Python overhead? For DFT it appears to be of the order of 2-3 ms, but very difficult to assess property.
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Do you have a specific thought on how to identify Python overhead? For DFT it appears to be of the order of 2-3 ms
I am note sure. There are options to run Python code using pyo3 (run
, eval
, from_code
, etc.) but I am not sure if we can use these to make proper comparisons or get an idea of the overhead of a function/method. 2-3 ms seems to be a lot, though. Does that include calling a property that returns a numpy array?
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