On Mon, Nov 6, 2017 at 9:36 PM, Stefan Krah <[email protected]> wrote: > On Mon, Nov 06, 2017 at 12:18:17PM +0200, Paul Sokolovsky wrote: >> MicroPython hashmap implementation is effectively O(n) (average and >> worst case) due to the algorithm parameters chosen (like the load factor >> of 1). Of course, parameters could be tweaked, but the ones chosen are >> so because the memory usage is far more important for MicroPython >> systems than performance characteristics (all due to small amounts of >> memory). Like, MicroPython was twice as fast than Python 3.3 on >> average, and 1000 times more efficient in the memory usage. > > $ cat xxx.py > > def pi_float(): > """native float""" > lasts, t, s, n, na, d, da = 0, 3.0, 3, 1, 0, 0, 24 > while s != lasts: > lasts = s > n, na = n+na, na+8 > d, da = d+da, da+32 > t = (t * n) / d > s += t > return s > > for i in range(100000): > x = pi_float() > > $ time ./micropython xxx.py > > real 0m4.424s > user 0m4.406s > sys 0m0.016s > $ > $ time ../../cpython/python xxx.py > > real 0m1.066s > user 0m1.056s > sys 0m0.010s > > > > Congratulations ...
Maybe I'm misreading your demo, but I fail to see what this has to do with dict performance? ChrisA _______________________________________________ Python-Dev mailing list [email protected] https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com