A new chapter for PyPy
PyPy winds down its membership in the Software Freedom Conservancy Conservancy and PyPy's great work together PyPy joined Conservancy in the second half of 2010, shortly after the release of PyPy 1.2, the first version to contain a fully functional JIT. In 2013, PyPy started supporting ARM, bringing its just-in-time speediness to many more devices and began working toward supporting NumPy to help
PyPy v7.2 released
The PyPy team is proud to release the version 7.2.0 of PyPy, which includes two different interpreters: PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.13 PyPy3.6: which is an interpreter supporting the syntax and the features of Python 3.6, including the stdlib for CPython 3.6.9. The interpreters are based on much the same
Automatic SIMD vectorization support in PyPy
Hi everyone, it took some time to catch up with the JIT refacrtorings merged in this summer. But, (drums) we are happy to announce that: The next release of PyPy, "PyPy 4.0.0", will ship the new auto vectorizer The goal of this project was to increase the speed of numerical applications in both the NumPyPy library and for arbitrary Python programs. In PyPy we have focused a lot on improvements in
NumPyPy progress report - running benchmarks
An important thing to notice is the data dependency of the inline version causes a huge slowdown for the C versions. This is not a severe disadvantage for us though -- the brain-dead Python version takes longer and PyPy is not able to take advantage of the knowledge that the data is independent. The results are in the same ballpark as the C versions -- 15% - 170% slower, but the algorithm one choo
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