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Binary wheels for PyPy

Originally published on the PyPy blog.

Hi,

this is a short blog post, just to announce the existence of this Github repository, which contains binary PyPy wheels for some selected packages. The availability of binary wheels means that you can install the packages much more quickly, without having to wait for compilation.

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Gothenburg sprint report

Originally published on the PyPy blog.

In the past week, we have been busy hacking on PyPy at the Gothenburg sprint, the second of this 2011. The sprint was hold at Laura's and Jacob's place, and here is a brief report of what happened.


In the first day we welcomed Mark Pearse, who was new to PyPy and at his first sprint. Mark worked the whole sprint in the new SpecialisedTuple branch, whose aim is to have a special implementation for small 2-items and 3-items tuples of primitive types (e.g., ints or floats) to save memory. Mark paired with Antonio for a couple of days, then he continued alone and did an amazing job. He even learned how to properly do Test Driven Development :-).

Antonio spent a couple of days investigating whether it is possible to use application checkpoint libraries such as BLCR and DMTCP to save the state of the PyPy interpreter between subsequent runs, thus saving also the JIT-compiled code to reduce the warmup time. The conclusion is that these are interesting technologies, but more work would be needed (either on the PyPy side or on the checkpoint library side) before it can have a practical usage for PyPy users.

Then, Antonio spent most of the rest of the sprint working on his ffistruct branch, whose aim is to provide a very JIT-friendly way to interact with C structures, and eventually implement ctypes.Structure on top of that. The "cool part" of the branch is already done, and the JIT already can compile set/get of fields into a single fast assembly instruction, about 400 times faster than the corresponding ctypes code. What is still left to do is to add a nicer syntax (which is easy) and to implement all the ctypes peculiarities (which is tedious, at best :-)).

As usual, Armin did tons of different stuff, including fixing a JIT bug, improving the performance of file.readlines() and working on the STM branch (for Software Transactional Memory), which is now able to run RPython multithreaded programs using software transaction (as long as they don't fill up all the memory, because support for the GC is still missing :-)). Finally, he worked on improving the Windows version of PyPy. While doing so he discovered together with Anto a terrible bug which lead to a continuous leak of stack space because the JIT called some functions using the wrong calling convention.

Håkan, with some help from Armin, worked on the jit-targets branch, whose goal is to heavily refactor the way the traces are internally represented by the JIT, so that in the end we can produce (even :-)) better code than what we do nowadays. More details in this mail.

Andrew Dalke worked on a way to integrate PyPy with FORTRAN libraries, and in particular the ones which are wrapped by Numpy and Scipy: in doing so, he wrote f2pypy, which is similar to the existing f2py but instead of producing a CPython extension module it produces a pure python modules based on ctypes. More work is needed before it can be considered complete, but f2pypy is already able to produce a wrapper for BLAS which passes most of the tests under CPython, although there's still work left to get it working for PyPy.

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Realtime image processing in Python

Originally published on the PyPy blog.

Image processing is notoriously a CPU intensive task. To do it in realtime, you need to implement your algorithm in a fast language, hence trying to do it in Python is foolish: Python is clearly not fast enough for this task. Is it? :-)
Actually, it turns out that the PyPy JIT compiler produces code which is fast enough to do realtime video processing using two simple algorithms implemented by Håkan Ardö.
sobel.py implements a classical way of locating edges in images, the Sobel operator. It is an approximation of the magnitude of the image gradient. The processing time is spend on two convolutions between the image and 3x3-kernels.
magnify.py implements a pixel coordinate transformation that rearranges the pixels in the image to form a magnifying effect in the center. It consists of a single loop over the pixels in the output image copying pixels from the input image.
You can try by yourself by downloading the appropriate demo: