PyPy 4.0.0¶
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 the PyPy 4.0.0 release here:
We would like to thank our donors for the continued support of the PyPy project.
We would also like to thank our contributors (7 new ones since PyPy 2.6.0) and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on pypy, or general help with making RPython’s JIT even better.
New Version Numbering¶
Since the past release, PyPy 2.6.1, we decided to update the PyPy 2.x.x versioning directly to PyPy 4.x.x, to avoid confusion with CPython 2.7 and 3.5. Note that this version of PyPy uses the stdlib and implements the syntax of CPython 2.7.10.
Vectorization¶
Richard Plangger began work in March and continued over a Google Summer of Code to add a vectorization step to the trace optimizer. The step recognizes common constructs and emits SIMD code where possible, much as any modern compiler does. This vectorization happens while tracing running code, so it is actually easier at run-time to determine the availability of possible vectorization than it is for ahead-of-time compilers.
Availability of SIMD hardware is detected at run time, without needing to precompile various code paths into the executable.
The first version of the vectorization has been merged in this release, since it is so new it is off by default. To enable the vectorization in built-in JIT drivers (like numpy ufuncs), add –jit vec=1, to enable all implemented vectorization add –jit vec_all=1
Benchmarks and a summary of this work appear here
Internal Refactoring: Warmup Time Improvement and Reduced Memory Usage¶
Maciej Fijalkowski and Armin Rigo refactored internals of rpython that now allow PyPy to more efficiently use guards in jitted code. They also rewrote unrolling, leading to a warmup time improvement of 20% or so. The reduction in guards also means a reduction in the use of memory, also a savings of around 20%.
Numpy¶
Our implementation of numpy continues to improve. ndarray and the numeric dtypes are very close to feature-complete; record, string and unicode dtypes are mostly supported. We have reimplemented numpy linalg, random and fft as cffi-1.0 modules that call out to the same underlying libraries that upstream numpy uses. Please try it out, especially using the new vectorization (via –jit vec=1 on the command line) and let us know what is missing for your code.
CFFI¶
While not applicable only to PyPy, cffi is arguably our most significant
contribution to the python ecosystem. Armin Rigo continued improving it,
and PyPy reaps the benefits of cffi-1.3: improved manangement of object
lifetimes, __stdcall on Win32, ffi.memmove(), and percolate const,
restrict keywords from cdef to C code.
What is PyPy?¶
PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7. It’s fast (pypy and cpython 2.7.x performance comparison) due to its integrated tracing JIT compiler.
We also welcome developers of other dynamic languages to see what RPython can do for them.
This release supports x86 machines on most common operating systems (Linux 32/64, Mac OS X 64, Windows 32, OpenBSD, freebsd), as well as newer ARM hardware (ARMv6 or ARMv7, with VFPv3) running Linux.
We also introduce support for the 64 bit PowerPC hardware, specifically Linux running the big- and little-endian variants of ppc64.
Other Highlights (since 2.6.1 release two months ago)¶
Bug Fixes
Applied OPENBSD downstream fixes
Fix a crash on non-linux when running more than 20 threads
In cffi, ffi.new_handle() is more cpython compliant
Accept unicode in functions inside the _curses cffi backend exactly like cpython
Fix a segfault in itertools.islice()
Use gcrootfinder=shadowstack by default, asmgcc on linux only
Fix ndarray.copy() for upstream compatability when copying non-contiguous arrays
Fix assumption that lltype.UniChar is unsigned
Fix a subtle bug with stacklets on shadowstack
Improve support for the cpython capi in cpyext (our capi compatibility layer). Fixing these issues inspired some thought about cpyext in general, stay tuned for more improvements
When loading dynamic libraries, in case of a certain loading error, retry loading the library assuming it is actually a linker script, like on Arch and Gentoo
Issues reported with our previous release were resolved after reports from users on our issue tracker at https://bitbucket.org/pypy/pypy/issues or on IRC at #pypy
New features:
Add an optimization pass to vectorize loops using x86 SIMD intrinsics.
Support __stdcall on Windows in CFFI
Improve debug logging when using PYPYLOG=???
Deal with platforms with no RAND_egd() in OpenSSL
Numpy:
Add support for ndarray.ctypes
Fast path for mixing numpy scalars and floats
Add support for creating Fortran-ordered ndarrays
Fix casting failures in linalg (by extending ufunc casting)
Recognize and disallow (for now) pickling of ndarrays with objects embedded in them
Performance improvements and refactorings:
Reuse hashed keys across dictionaries and sets
Refactor JIT interals to improve warmup time by 20% or so at the cost of a minor regression in JIT speed
Recognize patterns of common sequences in the JIT backends and optimize them
Make the garbage collecter more incremental over external_malloc() calls
Share guard resume data where possible which reduces memory usage
Fast path for zip(list, list)
Reduce the number of checks in the JIT for lst[a:]
Move the non-optimizable part of callbacks outside the JIT
Factor in field immutability when invalidating heap information
Unroll itertools.izip_longest() with two sequences
Minor optimizations after analyzing output from vmprof and trace logs
Remove many class attributes in rpython classes
Handle getfield_gc_pure* and getfield_gc_* uniformly in heap.py
Improve simple trace function performance by lazily calling fast2locals and locals2fast only if truly necessary
Please try it out and let us know what you think. We welcome feedback, we know you are using PyPy, please tell us about it!
Cheers
The PyPy Team