New issue 3142: Performance with simple numpy arrays https://bitbucket.org/pypy/pypy/issues/3142/performance-with-simple-numpy-arrays
Pierre Augier: I’m having a look at how much Python is bad with callbacks of tiny functions \([https://github.com/paugier/bench\_integrate\_callback](https://github.com/paugier/bench_integrate_callback)\). I was motivated by a benchmark from Julia people showing than Julia is much faster than Scipy for ODEs integration with tiny callback functions \([https://github.com/JuliaDiffEq/SciPyDiffEq.jl](https://github.com/JuliaDiffEq/SciPyDiffEq.jl)\). I thought that PyPy could be good to accelerate \`scipy.integrate.solve\_ivp\` since the code of solve\_ivp \([https://github.com/scipy/scipy/tree/master/scipy/integrate/\_ivp](https://github.com/scipy/scipy/tree/master/scipy/integrate/_ivp)\) is mostly “simple” Python using “simple” Numpy. However, it’s much slower with PyPy. I isolated a very simple case with a callback to check what happens. With a pure Python version \([https://github.com/paugier/bench\_integrate\_callback/blob/master/purepython\_callback.py](https://github.com/paugier/bench_integrate_callback/blob/master/purepython_callback.py)\), PyPy is 30 times faster than CPython! However, with a versions for which the time values are stored in a Numpy array \([https://github.com/paugier/bench\_integrate\_callback/blob/master/numpy\_callback.py](https://github.com/paugier/bench_integrate_callback/blob/master/numpy_callback.py)\), PyPy is 54 times slower than for the version for which the time values are stored in a list \(therefore nearly 2 times slower than CPython\). The only difference between the 2 scripts is \`times = dt \* np.arange\(1e5\)\` instead of \`times = \[dt \* it for it in range\(int\(1e5\)\)\]\`. Of course, most of the elapsed time is not spent in this line but in the loop. It’s sad that PyPy can’t accelerate Python code using simple Numpy. I realized that it is not something new, but still I’d like to ask if there is any chance that PyPy could be improved in that respect. _______________________________________________ pypy-issue mailing list pypy-issue@python.org https://mail.python.org/mailman/listinfo/pypy-issue
