PyPy 4.0.0 Released - A Jit with SIMD Vectorization and More
We’re pleased and proud to unleash PyPy 4.0.0, a major update of the PyPy python 2.7.10 compatible interpreter with a Just In Time compiler. We have improved warmup time and memory overhead used for tracing, added vectorization for numpy and general loops where possible on x86 hardware (disabled by default), refactored rough edges in rpython, and increased functionality of numpy. You can download
RPython can be faster than C
(yes, C as in language, not c as in speed of light). I looked recently at the great computer language shootout, for some benchmarks and to make some speed comparisons. I use this benchmark, modified it to be rpythonic-enough and compared speeds. The code is here (the only change from the Python version was to create a class instead of tuple, so actually this version is more OO). Also the benchmark
Plotting using matplotlib from PyPy
Big fat warning This is just a proof of concept. It barely works. There are missing pieces left and right, which were replaced with hacks so I can get this to run and prove it's possible. Don't try this at home, especially your home. You have been warned. There has been a lot of talking about PyPy not integrating well with the current scientific Python ecosystem, and numpypy (a NumPy reimplementat
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
