Making coverage.py faster under PyPy

If you've ever tried to run your programs with coverage.py under PyPy,
you've probably experienced some incredible slowness. Take this simple
program:

def f():
    return 1


def main():
    i = 10000000
    while i:
        i -= f()

main()

Running time coverage.py run test.py five times, and looking at the best
run, here's how PyPy 2.1 stacks up against CPython 2.7.5:

Python	Time	Normalized to CPython
CPython 2.7.5	3.879s	1.0x
PyPy 2.1	53.330s	13.7x slower

Totally ridiculous. I got turned onto this problem because on one of my
projects CPython takes about 1.5 minutes to run our test suite on the build
bot, but PyPy takes 8-10 minutes.

So I sat down to address it. And the results:

Python	Time	Normalized to CPython
CPython 2.7.5	3.879s	1.0x
PyPy 2.1	53.330s	13.7x slower
PyPy head	1.433s	2.7x faster

Not bad.

Technical details

So how'd we do it? Previously, using sys.settrace() (which coverage.py
uses under the hood) disabled the JIT. Except it didn't just disable the JIT,
it did it in a particularly insidious way — the JIT had no idea it was being
disabled!

Instead, every time PyPy discovered that one of your functions was a hotspot,
it would start tracing to observe what the program was doing, and right when it
was about to finish, coverage would run and cause the JIT to abort. Tracing
is a slow process, it makes up for it by generating fast machine code at the
end, but tracing is still incredibly slow. But we never actually got to the
"generate fast machine code" stage. Instead we'd pay all the cost of tracing,
but then we'd abort, and reap none of the benefits.

To fix this, we adjusted some of the heuristics in the JIT, to better show it
how sys.settrace(<tracefunc>) works. Previously the JIT saw it as an opaque
function which gets the frame object, and couldn't tell whether or not it
messed with the frame object. Now we let the JIT look inside the
<tracefunc> function, so it's able to see that coverage.py isn't
messing with the frame in any weird ways, it's just reading the line number and
file path out of it.

I asked several friends in the VM implementation and research field if they
were aware of any other research into making VMs stay fast when debugging tools
like coverage.py are running. No one I spoke to was aware of any (but I
didn't do a particularly exhaustive review of the literature, I just tweeted at
a few people), so I'm pleased to say that PyPy is quite possibly the first VM
to work on optimizing code in debugging mode! This is possible because of our
years spent investing in meta-tracing research.

Happy testing,
Alex

Update on STM

Hi all,

The sprint in London was a lot of fun and very fruitful. In the last update on STM, Armin was working on improving and specializing the automatic barrier placement. There is still a lot to do in that area, but that work is merged now. Specializing and improving barrier placement is still to be done for the JIT.

But that is not all. Right after the sprint, we were able to squeeze the last obvious bugs in the STM-JIT combination. However, the performance was nowhere near to what we want. So until now, we fixed some of the most obvious issues. Many come from RPython erring on the side of caution and e.g. making a transaction inevitable even if that is not strictly necessary, thereby limiting parallelism. Another problem came from increasing counters everytime a guard fails, which caused transactions to conflict on these counter updates. Since these counters do not have to be completely accurate, we update them non-transactionally now with a chance of small errors.

There are still many such performance issues of various complexity left to tackle: we are nowhere near done. So stay tuned or contribute :)

Performance

Now, since the JIT is all about performance, we want to at least show you some numbers that are indicative of things to come. Our set of STM benchmarks is very small unfortunately (something you can help us out with), so this is not representative of real-world performance. We tried to minimize the effect of JIT warm-up in the benchmark results.

The machine these benchmarks were executed on has 4 physical cores with Hyper-Threading (8 hardware threads).

Raytracer from stm-benchmarks: Render times in seconds for a 1024x1024 image:

Interpreter	Base time: 1 thread	8 threads (speedup)
PyPy-2.1	2.47	2.56 (0.96x)
CPython	81.1	73.4 (1.1x)
PyPy-STM	50.2	10.8 (4.6x)

For comparison, disabling the JIT gives 148s on PyPy-2.1 and 87s on PyPy-STM (with 8 threads).

Richards from PyPy repository on the stmgc-c4 branch: Average time per iteration in milliseconds:

Interpreter	Base time: 1 thread	8 threads (speedup)
PyPy-2.1	15.6	15.4 (1.01x)
CPython	239	237 (1.01x)
PyPy-STM	371	116 (3.2x)

For comparison, disabling the JIT gives 492ms on PyPy-2.1 and 538ms on PyPy-STM.

Try it!

All this can be found in the PyPy repository on the stmgc-c4 branch. Try it for yourself, but keep in mind that this is still experimental with a lot of things yet to come. Only Linux x64 is supported right now, but contributions are welcome.

You can download a prebuilt binary from here: https://bitbucket.org/pypy/pypy/downloads/pypy-oct13-stm.tar.bz2 (Linux x64 Ubuntu >= 12.04). This was made at revision bafcb0cdff48.

Summary

What the numbers tell us is that PyPy-STM is, as expected, the only of the three interpreters where multithreading gives a large improvement in speed. What they also tell us is that, obviously, the result is not good enough yet: it still takes longer on a 8-threaded PyPy-STM than on a regular single-threaded PyPy-2.1. However, as you should know by now, we are good at promising speed and delivering it... years later :-)

But it has been two years already since PyPy-STM started, and this is our first preview of the JIT integration. Expect major improvements soon: with STM, the JIT generates code that is completely suboptimal in many cases (barriers, allocation, and more). Once we improve this, the performance of the STM-JITted code should come much closer to PyPy 2.1.

Cheers

Remi & Armin

Incremental Garbage Collector in PyPy

Hello everyone.

We're pleased to announce that as of today, the default PyPy comes with a GC that has much smaller pauses than yesterday.

Let's start with explaining roughly what GC pauses are. In CPython each object has a reference count, which is incremented each time we create references and decremented each time we forget them. This means that objects are freed each time they become unreachable. That is only half of the story though. First note that when the last reference to a large tree of objects goes away, you have a pause: all the objects are freed. Your program is not progressing at all during this pause, and this pause's duration can be arbitrarily large. This occurs at deterministic times, though. But consider code like this:

class A(object):
     pass

a = A()
b = A()
a.item = b
b.item = a
del a
del b

This creates a reference cycle. It means that while we deleted references to a and b from the current scope, they still have a reference count of 1, because they point to each other, even though the whole group has no references from the outside. CPython employs a cyclic garbage collector which is used to find such cycles. It walks over all objects in memory, starting from some known roots, such as type objects, variables on the stack, etc. This solves the problem, but can create noticeable, nondeterministic GC pauses as the heap becomes large and convoluted.

PyPy essentially has only the cycle finder - it does not bother with reference counting, instead it walks alive objects every now and then (this is a big simplification, PyPy's GC is much more complex than this). Although this might sound like a missing feature, it is really one of the reasons why PyPy is so fast, because at the end of the day the total time spent in managing the memory is lower in PyPy than CPython. However, as a result, PyPy also has the problem of GC pauses.

To alleviate this problem, which is essential for applications like games, we started to work on incremental GC, which spreads the walking of objects and cleaning them across the execution time in smaller intervals. The work was sponsored by the Raspberry Pi foundation, started by Andrew Chambers and finished by Armin Rigo and Maciej Fijałkowski.

Benchmarks

Everyone loves benchmarks. We did not measure any significant speed difference on our quite extensive benchmark suite on speed.pypy.org. The main benchmark that we used for other comparisons was translating the topaz ruby interpreter using various versions of PyPy and CPython. The exact command was python <pypy-checkout>/bin/rpython -O2 --rtype targettopaz.py. Versions:

• topaz - dce3eef7b1910fc5600a4cd0afd6220543104823
• pypy source - defb5119e3c6
• pypy compiled with minimark (non-incremental GC) - d1a0c07b6586
• pypy compiled with incminimark (new, incremental GC) - 417a7117f8d7
• CPython - 2.7.3

The memory usage of CPython, PyPy with minimark and PyPy with incminimark is shown here. Note that this benchmark is quite bad for PyPy in general, the memory usage is higher and the amount of time taken is longer. This is due to the JIT warmup being both memory hungry and inefficient (see below). But first, the new GC is not worse than the old one.

EDIT:Red line is CPython, blue is incminimark (new), green is minimark (old)

The image was obtained by graphing the output of memusage.py.

However, the GC pauses are significantly smaller. For PyPy the way to get GC pauses is to measure time between start and stop while running stuff with PYPYLOG=gc-collect:log pypy program.py, for CPython, the magic incantation is gc.set_debug(gc.DEBUG_STATS) and parsing the output. For what is worth, the average and total for CPython, as well as the total number of events are not directly comparable since it only shows the cyclic collector, not the reference counts. The only comparable thing is the amount of long pauses and their duration. In the table below, pause duration is sorted into 8 buckets, each meaning "below that or equal to the threshold". The output is generated using the gcanalyze tool.

CPython:

150.1ms	300.2ms	450.3ms	600.5ms	750.6ms	900.7ms	1050.8ms	1200.9ms
5417	5	3	2	1	1	0	1

PyPy minimark (non-incremental GC):

216.4ms	432.8ms	649.2ms	865.6ms	1082.0ms	1298.4ms	1514.8ms	1731.2ms
27	14	6	4	6	5	3	3

PyPy incminimark (new incremental GC):

15.7ms	31.4ms	47.1ms	62.8ms	78.6ms	94.3ms	110.0ms	125.7ms
25512	122	4	1	0	0	0	2

As we can see, while there is still work to be done (the 100ms ones could be split among several steps), we did improve the situation quite drastically without any actual performance difference.

Note about the benchmark - we know it's a pretty extreme case of JIT warmup, we know we suck on it, we're working on it and we're not afraid of showing PyPy is not always the best ;-)

Nitty gritty details

Here are some nitty gritty details for people really interested in Garbage Collection. This was done as a patch to "minimark", our current GC, and called "incminimark" for now. The former is a generational stop-the-world GC. New objects are allocated "young", which means that they initially live in the "nursery", a special zone of a few MB of memory. When the nursery is full, a "minor collection" step moves the surviving objects out of the nursery. This can be done quickly (a few millisecond) because we only need to walk through the young objects that survive --- usually a small fraction of all young objects; and also by far not all objects that are alive at this point, but only the young ones. However, from time to time this minor collection is followed by a "major collection": in that step, we really need to walk all objects to classify which ones are still alive and which ones are now dead ("marking") and free the memory occupied by the dead ones ("sweeping"). You can read more details here.

This "major collection" is what gives the long GC pauses. To fix this problem we made the GC incremental: instead of running one complete major collection, we split its work into a variable number of pieces and run each piece after every minor collection for a while, until there are no more pieces. The pieces are each doing a fraction of marking, or a fraction of sweeping. It adds some few milliseconds after each of these minor collections, rather than requiring hundreds of milliseconds in one go.

The main issue is that splitting the major collections means that the main program is actually running between the pieces, and so it can change the pointers in the objects to point to other objects. This is not a problem for sweeping: dead objects will remain dead whatever the main program does. However, it is a problem for marking. Let us see why.

In terms of the incremental GC literature, objects are either "white", "gray" or "black". This is called tri-color marking. See for example this blog post about Rubinius, or this page about LuaJIT or the wikipedia description. The objects start as "white" at the beginning of marking; become "gray" when they are found to be alive; and become "black" when they have been fully traversed. Marking proceeds by scanning grey objects for pointers to white objects. The white objects found are turned grey, and the grey objects scanned are turned black. When there are no more grey objects, the marking phase is complete: all remaining white objects are truly unreachable and can be freed (by the following sweeping phase).

In this model, the important part is that a black object can never point to a white object: if the latter remains white until the end, it will be freed, which is incorrect because the black object itself can still be reached. How do we ensure that the main program, running in the middle of marking, will not try to write a pointer to white object into a black object? This requires a "write barrier", i.e. a piece of code that runs every time we set a pointer into an object or array. This piece of code checks if some (hopefully rare) condition is met, and calls a function if that is the case.

The trick we used in PyPy is to consider minor collections as part of the whole, rather than focus only on major collections. The existing minimark GC had always used a write barrier of its own to do its job, like any generational GC. This existing write barrier is used to detect when an old object (outside the nursery) is modified to point to a young object (inside the nursery), which is essential information for minor collections. Actually, although this was the goal, the actual write barrier code is simpler: it just records all old objects into which we write any pointer --- to a young or old object. As we found out over time, doing so is not actually slower, and might actually be a performance improvement: for example, if the main program does a lot of writes into the same old object, we don't need to check over and over again if the written pointer points to a young object or not. We just record the old object in some list the first time, and that's it.

The trick is that this unmodified write barrier works for incminimark too. Imagine that we are in the middle of the marking phase, running the main program. The write barrier will record all old objects that are being modified. Then at the next minor collection, all surviving young objects will be moved out of the nursery. At this point, as we're about to continue running the major collection's marking phase, we simply add to the list of pending gray objects all the objects that we just considered --- both the objects listed as "old objects that are being modified", and the objects that we just moved out of the nursery. A fraction from the former list were black object; so this mean that they are turned back from the black to the gray color. This technique implements nicely, if indirectly, what is called a "backward write barrier" in the literature. The backwardness is about the color that needs to be changed in the opposite of the usual direction "white -> gray -> black", thus making more work for the GC. (This is as opposed to "forward write barrier", where we would also detect "black -> white" writes but turn the white object gray.)

In summary, I realize that this description is less about how we turned minimark into incminimark, and more about how we differ from the standard way of making a GC incremental. What we really had to do to make incminimark was to write logic that says "if the major collection is in the middle of the marking phase, then add this object to the list of gray objects", and put it at a few places throughout minor collection. Then we simply split a major collection into increments, doing marking or sweeping of some (relatively arbitrary) number of objects before returning. That's why, after we found that the existing write barrier would do, it was not much actual work, and could be done without major changes. For example, not a single line from the JIT needed adaptation. All in all it was relatively painless work. ;-)

Cheers,
armin and fijal

Numpy Status Update

Hi everyone

Thanks to the people who donated money to the numpy proposal, here is what I've been working on recently :

- Fixed conversion from a numpy complex number to a python complex number
- Implement the rint ufunc
- Make numpy.character usable as a dtype
- Fix ndarray(dtype=str).fill()
- Various fixes on boolean and fancy indexing

Cheers
Romain

PyCon South Africa & sprint

Hi all,

For those of you that happen to be from South Africa: don't miss PyCon ZA 2013, next October 3rd and 4th! Like last year, a few of us will be there. There will be the first talk about STM getting ready (a blog post about that should follow soon).

Moreover, general sprints will continue on the weekend (5th and 6th). Afterwards, Fijal will host a longer PyPy sprint (marathon?) with me until around the 21th. You are welcome to it as well! Write to the mailing list or to fijal directly (fijall at gmail.com), or simply in comments of this post.

--- Armin

Slides of the PyPy London Demo Evening

The slides of the London demo evening are now online:

NumPy road forward

Hello everyone.

This is the roadmap for numpy effort in PyPy as discussed on the London sprint. First, the highest on our priority list is to finish the low-level part of the numpy module. What we'll do is to finish the RPython part of numpy and provide a pip installable numpypy repository that includes the pure python part of Numpy. This would contain the original Numpy with a few minor changes.

Second, we need to work on the JIT support that will make NumPy on PyPy faster. In detail:

• reenable the lazy loop evaluation
• optimize bridges, which is depending on optimizer refactorings
• SSE support

On the compatibility front, there were some independent attempts into making the following stuff working:

• f2py
• C API (in fact, PyArray_* API is partly present in the nightly builds of PyPy)
• matplotlib (both using PyArray_* API and embedding CPython runtime in PyPy)
• scipy

In order to make all of the above happen faster, it would be helpful to raise more funds. You can donate to PyPy's NumPy project on our website. Note that PyPy is a member of SFC which is a 501(c)(3) US non-profit, so donations from US companies can be tax-deducted.

Cheers,
fijal, arigo, ronan, rguillebert, anto and others

Preliminary London Demo Evening Agenda

We now have a preliminary agenda for the demo evening in London next week. It takes place on Tuesday, August 27 2013, 18:30-19:30 (BST) at King's College London, Strand. The preliminary agenda is as follows:

• Laurence Tratt: Welcome from the Software Development Team
• Carl Friedrich Bolz: A Short Introduction to PyPy
• Maciej Fijałkowski: Numpy on PyPy, Present State and Outlook
• Lukas Diekmann: Collection Strategies for Fast Containers in PyPy
• Armin Rigo: Software Transactional Memory for PyPy
• Edd Barrett: Unipycation: Combining Prolog and Python

All the talks are lightning talks. Afterwards there will be plenty of time for discussion.

There's still free spots, if you want to come, please register on the Eventbrite page. Hope to see you there!

Update on STM

Hi all,

A quick update on Software Transactional Memory. We are working on two fronts.

On the one hand, the integration of the "c4" C library with PyPy is done and works well, but is still subject to improvements. The "PyPy-STM" executable (without the JIT) seems to be stable, as far as it has been tested. It runs a simple benchmark like Richards with a 3.2x slow-down over a regular JIT-less PyPy.

The main factor of this slow-down: the numerous "barriers" in the code --- checks that are needed a bit everywhere to verify that a pointer to an object points to a recent enough version, and if not, to go to the most recent version. These barriers are inserted automatically during the translation; there is no need for us to manually put 42 million barriers in the source code of PyPy. But this automatic insertion uses a primitive algorithm right now, which usually ends up putting more barriers than the theoretical optimum. I (Armin) am trying to improve that --- and progressing: last week the slow-down was around 4.5x. This is done in the branch stmgc-static-barrier.

On the other hand, Remi is progressing on the JIT integration in the branch stmgc-c4. This has been working in simple cases since a couple of weeks by now, but the resulting "PyPy-JIT-STM" often crashes. This is because while the basics are not really hard, we keep hitting new issues that must be resolved.

The basics are that whenever the JIT is about to generate assembler corresponding to a load or a store in a GC object, it must first generate a bit of extra assembler that corresponds to the barrier that we need. This works fine by now (but could benefit from the same kind of optimizations described above, to reduce the number of barriers). The additional issues are all more subtle. I will describe the current one as an example: it is how to write constant pointers inside the assembler.

Remember that the STM library classifies objects as either "public" or "protected/private". A "protected/private" object is one which has not been seen by another thread so far. This is essential as an optimization, because we know that no other thread will access our protected or private objects in parallel, and thus we are free to modify their content in place. By contrast, public objects are frozen, and to do any change, we first need to build a different (protected) copy of the object. See this blog post for more details.

So far so good, but the JIT will sometimes (actually often) hard-code constant pointers into the assembler it produces. For example, this is the case when the Python code being JITted creates an instance of a known class; the corresponding assembler produced by the JIT will reserve the memory for the instance and then write the constant type pointer in it. This type pointer is a GC object (in the simple model, it's the Python class object; in PyPy it's actually the "map" object, which is a different story).

The problem right now is that this constant pointer may point to a protected object. This is a problem because the same piece of assembler can later be executed by a different thread. If it does, then this different thread will create instances whose type pointer is bogus: looking like a protected object, but actually protected by a different thread. Any attempt to use this type pointer to change anything on the class itself will likely crash: the threads will all think they can safely change it in-place. To fix this, we need to make sure we only write pointers to public objects in the assembler. This is a bit involved because we need to ensure that there is a public version of the object to start with.

When this is done, we will likely hit the next problem, and the next one; but at some point it should converge (hopefully!) and we'll give you our first PyPy-JIT-STM ready to try. Stay tuned :-)

A bientôt,

Armin.

NumPyPy Status Update

Hello everyone

As expected, nditer is a lot of work. I'm going to pause my work on it for now and focus on simpler and more important things, here is a list of what I implemented :

• Fixed a bug on 32 bit that made int32(123).dtype == dtype("int32") fail
• Fixed a bug on the pickling of array slices
• The external loop flag is implemented on the nditer class
• The c_index, f_index and multi_index flags are also implemented
• Add dtype("double") and dtype("str")
• C-style iteration is available for nditer

Cheers
Romain Guillebert

PyPy 2.1 - Considered ARMful

We're pleased to announce PyPy 2.1, which targets version 2.7.3 of the Python
language. This is the first release with official support for ARM processors in the JIT.
This release also contains several bugfixes and performance improvements.

You can download the PyPy 2.1 release here:

http://pypy.org/download.html

We would like to thank the Raspberry Pi Foundation for supporting the work
to finish PyPy's ARM support.

The first beta of PyPy3 2.1, targeting version 3 of the Python language, was
just released, more details can be found here.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7. It's fast (pypy 2.1 and cpython 2.7.2 performance comparison) due to its integrated tracing JIT compiler.

This release supports x86 machines running Linux 32/64, Mac OS X 64 or Windows 32. This release also supports ARM machines running Linux 32bit - anything with ARMv6 (like the Raspberry Pi) or ARMv7 (like the Beagleboard, Chromebook, Cubieboard, etc.) that supports VFPv3 should work. Both hard-float armhf/gnueabihf and soft-float armel/gnueabi builds are provided. The armhf builds for Raspbian are created using the Raspberry Pi custom cross-compilation toolchain based on gcc-arm-linux-gnueabihf and should work on ARMv6 and ARMv7 devices running Debian or Raspbian. The armel builds are built using the gcc-arm-linux-gnuebi toolchain provided by Ubuntu and currently target ARMv7.

Windows 64 work is still stalling, we would welcome a volunteer to handle that.

Highlights

• JIT support for ARM, architecture versions 6 and 7, hard- and soft-float ABI
• Stacklet support for ARM
• Support for os.statvfs and os.fstatvfs on unix systems
• Improved logging performance
• Faster sets for objects
• Interpreter improvements
• During packaging, compile the CFFI based TK extension
• Pickling of numpy arrays and dtypes
• Subarrays for numpy
• Bugfixes to numpy
• Bugfixes to cffi and ctypes
• Bugfixes to the x86 stacklet support
• Fixed issue 1533: fix an RPython-level OverflowError for space.float_w(w_big_long_number).
• Fixed issue 1552: GreenletExit should inherit from BaseException.
• Fixed issue 1537: numpypy __array_interface__
• Fixed issue 1238: Writing to an SSL socket in PyPy sometimes failed with a "bad write retry" message.

Cheers,

David Schneider for the PyPy team.

PyPy Demo Evening in London, August 27, 2013

As promised in the London sprint announcement we are organising a PyPy demo evening during the London sprint on Tuesday, August 27 2013, 18:30-19:30 (BST). The description of the event is below. If you want to come, please register on the Eventbrite page.

---

PyPy is a fast Python VM. Maybe you've never used PyPy and want to find out what use it might be for you? Or you and your organisation have been using it and you want to find out more about how it works under the hood? If so, this demo session is for you!

Members of the PyPy team will give a series of lightning talks on PyPy: its benefits; how it works; research currently being undertaken to make it faster; and unusual uses it can be put to. Speakers will be available afterwards for informal discussions. This is the first time an event like this has been held in the UK, and is a unique opportunity to speak to core people. Speakers confirmed thus far include: Armin Rigo, Maciej Fijałkowski, Carl Friedrich Bolz, Lukas Diekmann, Laurence Tratt, Edd Barrett.

The venue for this talk is the Software Development Team, King's College London. The main entrance is on the Strand, from where the room for the event will be clearly signposted. Travel directions can be found at http://www.kcl.ac.uk/campuslife/campuses/directions/strand.aspx

If you have any questions about the event, please contact Laurence Tratt

PyPy3 2.1 beta 1

We're pleased to announce the first beta of the upcoming 2.1 release of
PyPy3. This is the first release of PyPy which targets Python 3 (3.2.3)
compatibility.

We would like to thank all of the people who donated to the py3k proposal
for supporting the work that went into this and future releases.

You can download the PyPy3 2.1 beta 1 release here:

http://pypy.org/download.html#pypy3-2-1-beta-1

Highlights

• The first release of PyPy3: support for Python 3, targetting CPython 3.2.3!
  • There are some known issues including performance regressions (issues
    #1540 & #1541) slated to be resolved before the final release.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for
CPython 2.7.3 or 3.2.3. It's fast due to its integrated tracing JIT compiler.

This release supports x86 machines running Linux 32/64, Mac OS X 64 or Windows
32. Also this release supports ARM machines running Linux 32bit - anything with
ARMv6 (like the Raspberry Pi) or ARMv7 (like Beagleboard,
Chromebook, Cubieboard, etc.) that supports VFPv3 should work.

Windows 64 work is still stalling and we would welcome a volunteer to handle
that.

How to use PyPy?

We suggest using PyPy from a virtualenv. Once you have a virtualenv
installed, you can follow instructions from pypy documentation on how
to proceed. This document also covers other installation schemes.

Cheers,
the PyPy team

PyPy 2.1 beta 2

We're pleased to announce the second beta of the upcoming 2.1 release of PyPy.
This beta adds one new feature to the 2.1 release and contains several bugfixes listed below.

You can download the PyPy 2.1 beta 2 release here:

http://pypy.org/download.html

Highlights

• Support for os.statvfs and os.fstatvfs on unix systems.
• Fixed issue 1533: fix an RPython-level OverflowError for space.float_w(w_big_long_number).
• Fixed issue 1552: GreenletExit should inherit from BaseException.
• Fixed issue 1537: numpypy __array_interface__
• Fixed issue 1238: Writing to an SSL socket in pypy sometimes failed with a "bad write retry" message.
• distutils: copy CPython's implementation of customize_compiler, dont call
  split on environment variables, honour CFLAGS, CPPFLAGS, LDSHARED and
  LDFLAGS.
• During packaging, compile the CFFI tk extension.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for
CPython 2.7.3. It's fast due to its integrated tracing JIT compiler.

This release supports x86 machines running Linux 32/64, Mac OS X 64 or Windows
32. Also this release supports ARM machines running Linux 32bit - anything with
ARMv6 (like the Raspberry Pi) or ARMv7 (like Beagleboard,
Chromebook, Cubieboard, etc.) that supports VFPv3 should work.

Windows 64 work is still stalling, we would welcome a volunteer
to handle that.

How to use PyPy?

We suggest using PyPy from a virtualenv. Once you have a virtualenv
installed, you can follow instructions from pypy documentation on how
to proceed. This document also covers other installation schemes.

Cheers,
The PyPy Team.

PyPy San Francisco Sprint July 27th 2013

The next PyPy sprint will be in San Francisco, California. It is a public
sprint, suitable for newcomers. It will run on Saturday July 27th.

Some possible things people will be hacking on the sprint:

• running your software on PyPy
• making your software fast on PyPy
• improving PyPy's JIT
• improving Twisted on PyPy
• any exciting stuff you can think of

If there are newcomers, we'll run an introduction to hacking on PyPy.

Location
The sprint will be held at the Rackspace Office:

620 Folsom St, Ste 100

The doors will open at 10AM and run until 6PM.

PyPy London Sprint (August 26 - September 1 2013)

The next PyPy sprint will be in London, United Kingdom for the first time. This is a fully public sprint. PyPy sprints are a very good way to get into PyPy development and no prior PyPy knowledge is necessary.

Goals and topics of the sprint

For newcomers:

• bring your application/library and we'll help you port it to PyPy, benchmark and profile
• come and write your favorite missing numpy function
• help us work on developer tools like jitviewer

We'll also work on:

• refactoring the JIT optimizations
• STM and STM-related topics
• anything else attendees are interested in

Exact times

The work days should be August 26 - September 1 2013 (Monday-Sunday). The official plans are for people to arrive on the 26th, and to leave on the 2nd. There will be a break day in the middle. We'll typically start at 10:00 in the morning.

Location

The sprint will happen within a room of King's College's Strand Campus in Central London, UK. There are some travel instructions how to get there. We are being hosted by Laurence Tratt and the Software Development Team.

Demo Session

If you don't want to come to the full sprint, but still want to chat a bit, we are planning to have a demo session on Tuesday August 27. We will announce this separately on the blog. If you are interested, please leave a comment.

Registration

If you want to attend, please register by adding yourself to the "people.txt" file in Mercurial:

https://bitbucket.org/pypy/extradoc/
https://bitbucket.org/pypy/extradoc/raw/extradoc/sprintinfo/london-2013

or on the pypy-dev mailing list if you do not yet have check-in rights:

http://mail.python.org/mailman/listinfo/pypy-dev

Remember that you may need a (insert country here)-to-UK power adapter. Please note that UK is not within the Schengen zone, so non-EU and non-Switzerland citizens may require specific visa. Please check travel regulations. Also, the UK uses pound sterling (GBP).

Software Transactional Memory lisp experiments

As covered in the previous blog post, the STM subproject of PyPy has been back on the drawing board. The result of this experiment is an STM-aware garbage collector written in C. This is finished by now, thanks to Armin's and Remi's work, we have a fully functional garbage collector and a STM system that can be used from any C program with enough effort. Using it is more than a little mundane, since you have to inserts write and read barriers by hand everywhere in your code that reads or writes to garbage collector controlled memory. In the PyPy integration, this manual work is done automatically by the STM transformation in the interpreter.

However, to experiment some more, we created a minimal lisp-like/scheme-like interpreter (called Duhton), that follows closely CPython's implementation strategy. For anyone familiar with CPython's source code, it should be pretty readable. This interpreter works like a normal and very basic lisp variant, however it comes with a transaction builtin, that lets you spawn transactions using the STM system. We implemented a few demos that let you play with the transaction system. All the demos are running without conflicts, which means there are no conflicting writes to global memory and hence the demos are very amenable to parallelization. They exercise:

• arithmetics - demo/many_sqare_roots.duh
• read-only access to globals - demo/trees.duh
• read-write access to local objects - demo/trees2.duh

With the latter ones being very similar to the classic gcbench. STM-aware Duhton can be found in the stmgc repo, while the STM-less Duhton, that uses refcounting, can be found in the duhton repo under the base branch.

Below are some benchmarks. Note that this is a little comparing apples to oranges since the single-threaded duhton uses refcounting GC vs generational GC for STM version. Future pypy benchmarks will compare more apples to apples. Moreover none of the benchmarks has any conflicts. Time is the total time that the benchmark took (not the CPU time) and there was very little variation in the consecutive runs (definitely below 5%).

benchmark	1 thread (refcount)	1 thread (stm)	2 threads	4 threads
square	1.9s	3.5s	1.8s	0.9s
trees	0.6s	1.0s	0.54s	0.28s
trees2	1.4s	2.2s	1.1s	0.57s

As you can see, the slowdown for STM vs single thread is significant (1.8x, 1.7x, 1.6x respectively), but still lower than 2x. However the speedup from running on multiple threads parallelizes the problem almost perfectly.

While a significant milestone, we hope the next blog post will cover STM-enabled pypy that's fully working with JIT work ongoing.

Cheers,
fijal on behalf of Remi Meier and Armin Rigo

PyPy 2.1 beta

We're pleased to announce the first beta of the upcoming 2.1 release of PyPy. This beta contains many bugfixes and improvements, numerous improvements to the numpy in pypy effort. The main feature being that the ARM processor support is not longer considered alpha level.

We would like to thank the Raspberry Pi Foundation for supporting the work to finish PyPy's ARM support.


You can download the PyPy 2.1 beta release here:

http://pypy.org/download.html

Highlights

• Bugfixes to the ARM JIT backend, so that ARM is now an officially
  supported processor architecture
• Stacklet support on ARM
• Interpreter improvements
• Various numpy improvements
• Bugfixes to cffi and ctypes
• Bugfixes to the stacklet support
• Improved logging performance
• Faster sets for objects

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7.3. It's fast due to its integrated tracing JIT compiler. This release supports x86 machines running Linux 32/64, Mac OS X 64 or Windows 32. Also this release supports ARM machines running Linux 32bit - anything with ARMv6 (like the Raspberry Pi) or ARMv7 (like Beagleboard, Chromebook, Cubieboard, etc.) that supports VFPv3 should work. Both hard-float armhf/gnueabihf and soft-float armel/gnueabi builds are provided. armhf builds for Raspbian are created using the Raspberry Pi
custom cross-compilation toolchain based on gcc-arm-linux-gnueabihf and should work on ARMv6 and ARMv7 devices running Debian or Raspbian. armel builds are built using the gcc-arm-linux-gnuebi toolchain provided by Ubuntu and currently target ARMv7.

Windows 64 work is still stalling, we would welcome a volunteer to handle that.

How to use PyPy?

We suggest using PyPy from a virtualenv. Once you have a virtualenv installed, you can follow instructions from pypy documentation on how to proceed. This document also covers other installation schemes.

Cheers,

the PyPy team.

EuroPython

Hi all,

A short note: if you're at EuroPython right now and wondering if PyPy is dead because you don't see the obviously expected talk about PyPy, don't worry. PyPy is still alive and kicking. The truth is two-fold: (1) we missed the talk deadline (duh!)... but as importantly, (2) for various reasons we chose not to travel to Florence this year after our trip to PyCon US. (Antonio Cuni is at Florence but doesn't have a talk about PyPy either.)

Armin

Py3k status update #11

This is the 11th status update about our work on the py3k branch, which we
can work on thanks to all of the people who donated to the py3k proposal.

Here's some highlights of the progress made since the previous update:

• PyPy py3k now matches CPython 3's hash code for
  int/float/complex/Decimal/Fraction
• Various outstanding unicode identifier related issues were
  resolved. E.g. test_importlib/pep263/ucn/unicode all now fully pass. Various
  usage of identifiers (in particular type and module names) have been fixed to
  handle non-ascii names -- mostly around display of reprs and exception
  messages.
• The unicodedata database has been upgraded to 6.0.0.
• Windows support has greatly improved, though it could still use some more
  help (but so does the default branch to a certain degree).
• Probably the last of the parsing related bugs/features have been taken care
  of.
• Of course various other smaller miscellaneous fixes

This leaves the branch w/ only about 5 outstanding failures of the stdlib test
suite:

• test_float

  1 failing test about containment of floats in collections.

• test_memoryview

  Various failures: requires some bytes/str changes among other things (Manuel
  Jacob's has some progress on this on the py3k-memoryview branch)

• test_multiprocessing

  1 or more tests deadlock on some platforms

• test_sys and test_threading

  2 failing tests for the New GIL's new API

Probably the biggest feature left to tackle is the New GIL.

We're now pretty close to pushing an initial release. We had planned for one
around PyCon, but having missed that we've put some more effort into the branch
to provide a more fully-fledged initial release.

Thanks to the following for their contributions: Manuel Jacob, Amaury Forgeot
d'Arc, Karl Ramm, Jason Chu and Christian Hudon.

cheers,
Phil