The first release candidate for PyPy7.3.4 is up at https://downloads.python.org/pypy/. People using cibuildwheel or github actions to test PyPy should be able to use this in their test runs. The sha256 checksums can be found in the PR to the pypy.org web site at https://github.com/pypy/pypy.org/pull/13/files#diff-622ff636d3511de2e408d8b4... or via the preview at https://deploy-preview-13--keen-mestorf-442210.netlify.app/download_advanced... The draft release note is up at https://doc.pypy.org/en/latest/release-v7.3.4.html This is an incrementally better release: there are no ground-breaking changes. However there are a number of JIT preformance enhancements, and overall better compatibility with CPython. Please try it out and report issues on IRC or on the issue tracker. Matti
Thank a lot Matti and all PyPy developers for the work done on this release, Two small remarks on the sentence in the release note: "There are also some significant performance improvements around maps (dictionaries), ints, strings, btyes and more. These were done as users reported reproducible performance problems." 1. typo "btyes" 2. It is also the first PyPy release to take advantage of "type freezing", a specialization for objects with type stable attributes (described in https://dl.acm.org/doi/abs/10.1145/3368826.3377907) which greatly improves efficiency of numerically oriented code using a lot of small Python objects containing numbers. With PyPy 7.3.4, a pure Python implementation of the NBody problem (bench_purepy_Particle.py, see https://github.com/paugier/nbabel#table-of-codes) is basically as fast as our implementations in C++ and Fortran. Pierre ----- Mail original -----
Thank a lot Matti and all PyPy developers for the work done on this release, Two small remarks on the sentence in the release note: "There are also some significant performance improvements around maps (dictionaries), ints, strings, btyes and more. These were done as users reported reproducible performance problems." 1. typo "btyes" 2. It is also the first PyPy release to take advantage of "type freezing", a specialization for objects with type stable attributes (described in https://dl.acm.org/doi/abs/10.1145/3368826.3377907) which greatly improves efficiency of numerically oriented code using a lot of small Python objects containing numbers. With PyPy 7.3.4, a pure Python implementation of the NBody problem (bench_purepy_Particle.py, see https://github.com/paugier/nbabel#table-of-codes) is basically as fast as our implementations in C++ and Fortran. Pierre ----- Mail original -----
participants (2)
-
Matti Picus -
PIERRE AUGIER