Hi Toni, On 6 November 2014 23:19, Toni Mattis <toni.mat...@student.hpi.uni-potsdam.de> wrote: > that sounds more convenient than manipulating floats as architecture > dependent integers ;) So this is my implementation now: > > https://bitbucket.org/amintos/pypy/commits/f30efb9a8e54e56af7e7a0d07ec19d6985c1f4e0?at=float-opt
Thanks! Do you mind if I merge this branch 'float-ops' into the standard repo? Or are you a person who wouldn't like to see the first attempt show up in the history? (Generally, histories are good to have, and PyPy's contains tons of half-way or reverted checkins.) > BTW, the jit log (PYPYLOG=jit-log-opt,jit-backend) tends to round small > floats like '1.0 / 8.98846567431158e+307' to '0.000000', but '1.0 / > 2.2250738585072014e-308' appears as a full 308-digit decimal number. > This may cause some confusion when checking where the optimization is > effective. Ah, yes. Maybe it would be better if the jit log showed floats with full precision. I think it's caused by `str(arg.getfloat())` in metainterp/logger.py. We could use instead `rfloat.double_to_string(x, 'r', 0, 0)[0]`. A bientôt, Armin. _______________________________________________ pypy-dev mailing list pypy-dev@python.org https://mail.python.org/mailman/listinfo/pypy-dev
