Tuesday, August 30, 2011

Wrapping C++ Libraries with Reflection — Status Report One Year Later

Well over a year ago, work was started on the cppyy module which lives in the reflex-support branch. Since then, work has progressed at a varying pace and has included a recent sprint in Düsseldorf, last July.

Let's first take a step back and recap why we're interested in doing this, given that it is perfectly possible to use C++ through generated bindings and cpyext. cppyy makes use of reflection information generated for the C++ classes of interest, and has that reflection information available at run time. Therefore, it is able to open up complex C++ types to the JIT in a conceptually similar manner as simple types are open to it. This means that it is possible to get rid of a lot of the marshalling layers when making cross-language calls, resulting in much lower call overhead than is possible when going through the CPython API, or other methods of wrapping.

There are two problems that need to be solved: C++ language constructs need to be presented on the Python side in a natural way; and cross-language impedance mismatches need to be minimized, with some hints of the user if need be. For the former, the list of mapped features has grown to a set that is sufficient to do real work. There is now support for:

  • builtin, pointer, and array types
  • namespaces, classes, and inner classes
  • global functions, global data
  • static/instance data members and methods
  • default variables, object return by value
  • single and multiple (virtual) inheritance
  • templated classes
  • basic STL support and pythonizations
  • basic (non-global) operator mapping

The second problem is harder and will always be an on-going process. But one of the more important issues has been solved at the recent Düsseldorf sprint, namely, that of reclaiming C++ objects instantiated from the Python side by the garbage collector.

Performance has also improved, especially that of the nicer "pythonized" interface that the user actually sees, although it still misses out on about a factor of 2.5 in comparison to the lower-level interface (which has gotten uglier, so you really don't want to use that). Most of this improvement is due to restructuring so that it plays nicer with the JIT and libffi, both of which themselves have seen improvements.

Work is currently concentrated on the back-ends: a CINT back-end is underway and a LLVM/CLang pre-compiled headers (PCH) back-end is planned. The latter is needed for this code to be released in the wild, rather than just used in high energy physics (HEP), as that would be easier to support. Also, within HEP, CLang's PCH are foreseen to be the future format of reflection information.

At the end of the Düsseldorf sprint, we tried a little code that did something actually "useful," namely the filling of a histogram with some random values. We did get it to work, but trying cppyy on a large class library showed that a good warning system for such things like missing classes was sorely needed. That has been added since, and revisiting the histogram example later, here is an interesting note: the pypy-c run takes 1.5x the amount of time of that of the compiled, optimized, C++ code. The run was timed start to finish, including the reflection library loading and JIT warm-up that is needed in the case of Python, but not for the compiled C++ code. However, in HEP, scientists run many short jobs while developing their analysis codes, before submitting larger jobs on the GRID to run during lunch time or overnight. Thus, a more realistic comparison is to include the compilation time needed for the C++ code and with that, the Python code needs only 55% of the time required by C++.

The choice of a programming language is often a personal one, and such arguments like the idea that C++ is hard to use typically do not carry much weight with the in-crowd that studies quantum field dynamics for fun. However, getting the prompt with your analysis results back faster is a sure winner. We hope that cppyy will soon have progressed far enough to make it useful first to particle physicists and then other uses for wrapping C++ libraries.

Wim Lavrijsen, Carl Friedrich Bolz, Armin Rigo

Tuesday, August 23, 2011

We need Software Transactional Memory

Hi all. Here is (an extract of) a short summary paper about my current position on Software Transactional Memory as a general tool in the implementation of Python or Python-like languages. Thanks to people on IRC for discussion on making this blog post better (lucian, Alex Gaynor, rguillebert, timonator, Da_Blitz). For the purpose of the present discussion, we are comparing Java with Python when it comes to multi-threading.

The problem in complex high-level languages

Like Java, the Python language gives guarantees: it is not acceptable for the Python virtual machine to crash due to incorrect usage of threads. A primitive operation in Java is something like reading or writing a field of an object; the corresponding guarantees are along the lines of: if the program reads a field of an object, and another thread writes to the same field of the same object, then the program will see either the old value, or the new value, but not something else entirely, and the virtual machine will not crash.

Higher-level languages like Python differ from Java by the fact that a "primitive operation" is far more complex. It may for example involve looking in several hash maps, perhaps doing updates. In general, it is completely impossible to map every operation that must be atomic to a single processor instruction.

Jython: fine-grained locking

This problem has been solved "explicitly" in the Jython interpreter that runs on top of Java. The solution is explicit in the following sense: throughout the Jython interpreter, every single operation makes careful use of Java-level locking mechanisms. This is an application of "fine-grained locking". For example, operations like attribute lookup, which need to perform look-ups in a number of hash maps, are protected by acquiring and releasing locks (in __getattribute__).

A draw-back of this solution is the attention to detail required. If even one place misses a lock, then there is either a bug --- and such bugs occur in cases that are increasingly rare and hard to debug as the previous bugs are fixed --- or we just file it under "differences from CPython". There is however the risk of deadlock, if two threads attempt to lock the same objects in different order.

In practice, the situation is actually not as bad as I may paint it: the number of locks in Jython is reasonable, and allows for all the "common cases" to work as expected. (For the uncommon cases, see below.)

Performance-wise, the Java virtual machine itself comes with locks that have been heavily optimized over a long period of time, so the performance is acceptable. However if this solution were coded in C, it would need a lot of extra work to optimize the locks manually (possibly introducing more of the subtle bugs).

CPython: coarse-grained locking

CPython, the standard implementation of Python in C, took a different and simpler approach: it has a single global lock, called the Global Interpreter Lock (GIL). It uses "coarse-grained locking": the lock is acquired and released around the whole execution of one bytecode (or actually a small number of bytecodes, like 100). This solution is enough to ensure that no two operations can conflict with each other, because the two bytecodes that invoke them are themselves serialized by the GIL. It is a solution which avoids --- unlike Jython --- writing careful lock-acquiring code all over the interpreter. It also offers even stronger guarantees: every bytecode runs entirely atomically.

Nowadays, the draw-back of the GIL approach is obvious on multi-core machines: by serializing the execution of bytecodes, starting multiple threads does not actually let the interpreter use of more than one core.

PyPy, the Python implementation in Python, takes the same approach so far.

Existing usage

As we have seen, we have the following situation: the existing Python language, as CPython implements it, offers very strong guarantees about multi-threaded usage. It is important to emphasize that most existing multi-threaded Python programs actually rely on such strong guarantees. This can be seen for example in a problem that takes a populated list and does in several threads:

next_item = global_list.pop()

This implicitly relies on the fact that pop() will perform atomic removal from the list. If two threads try to pop() from the same list at the same time, then the two operations will occur in one order or the other; but they will not e.g. return the same object to both threads or mess up the internal state of the list object.

With such an example in mind, it should be clear that we do not want a solution to the multi-core issue that involves dropping these strong guarantees. It is ok however to lower the barrier, as Jython does; but any Python implementation must offer some guarantees, or not offer multi-threading at all. This includes the fact that a lot of methods on built-in types are supposed to be atomic.

(It should be noted that not offering multi-threading at all is actually also a (partial) solution to the problem. Recently, several "hacks" have appeared that give a programmer more-or-less transparent access to multiple independent processes (e.g. multiprocessing). While these provide appropriate solutions in some context, they are not as widely applicable as multi-threading. As a typical example, they fail to apply when the mutiple cores need to process information that cannot be serialized at all --- a requirement for any data exchange between several processes.)

Here is an example of how Jython's consistency is weaker than CPython's GIL. It takes uncommon examples to show it, and the fact that it does not work like a CPython programmer expect them to is generally considered as an implementation detail. Consider:

Thread 1:  set1.update(set2)
Thread 2:  set2.update(set3)
Thread 3:  set3.update(set1)

Each operation is atomic in the case of CPython, but decomposed in two steps (which can each be considered atomic) in the case of Jython: reading from the argument, and then updating the target set. Suppose that initially set1 = {1}, set2 = {2}, set3 = {3}. On CPython, independently on the order in which the threads run, we will end up with at least one of the sets being {1, 2, 3}. On Jython, it is possible that all three sets end up as containing two items only. The example is a bit far-fetched but should show that CPython's consistency is strictly stronger than Jython's.


PyPy is a Python interpreter much like CPython or Jython, but the way it is produced is particular. It is an interpreter written in RPython, a subset of Python, which gets turned into a complete virtual machine (as generated C code) automatically by a step called the "translation". In this context, the trade-offs are different from the ones in CPython and in Jython: it is possible in PyPy, and even easy, to apply arbitrary whole-program transformations to the interpreter at "translation-time".

With this in mind, it is possible to imagine a whole-program transformation that would add locking on every object manipulated in RPython by the interpreter. This would end up in a situation similar to Jython. However, it would not automatically solve the issue of deadlocks, which is avoided in the case of Jython by careful manual placement of the locks. (In fact, being deadlock-free is a global program property that cannot be automatically ensured or verified; any change to Jython can in theory break this property, and thus introduce subtle deadlocks. The same applies to non-atomicity.)

In fact, we can easily check that if the interpreter accesses (for both reading and writing) objects A and B in a bytecode of thread 1, and objects B and A (in the opposite order) in a bytecode of thread 2 --- and moreover if you need to have accessed the first object before you can decide that you will need to access the second object --- then there is no way (apart from the GIL) to avoid a deadlock while keeping the strong guarantee of atomicity. Indeed, if both threads have progressed to the middle of the execution of their bytecode, then A has already been mutated by thread 1 and similarly B has already been mutated by thread 2. It is not possible to successfully continue running the threads in that case.

Using Software Transactional Memory

Software Transactional Memory (STM) is an approach that gives a solution to precisely the above problem. If a thread ended up in a situation where continuing to run it would be wrong, then we can abort and rollback. This is similar to the notion of transaction on databases. In the above example, one or both threads would notice that they are about to run into troubles and abort. This means more concretely that they need to have a way to restart execution at the start of the bytecode, with all the side-effects of what they did so far being either cancelled or just not committed yet.

We think that this capacity to abort and rollback is the missing piece of the puzzle of multi-threaded implementations of Python. Actually, according to the presentation of the problem given above, it is unavoidable that any solution that wants to offer the same level of consistency and atomicity as CPython would involve the capacity of aborting and rolling back --- which means precisely that STM cannot be avoided.

Ok, but why not settle down with Jython's approach and put careful locks left and right throughout the interpreter? Because (1) we would have to consider every operation's atomicity and make decisions (or steal Jython's) and document them here; (2) it would also be really a lot of work, to optimize these locks e.g. with the JIT as well as the JVM does; and (3) it is not the PyPy way to require manually tweaking your code everywhere for a feature that should be orthogonal. Point (3) is probably the most important here: you need to redo the work for every language you implement in PyPy. It also implies my own point (4): it is not fun :-)

In more details, the process would work as follows. (This gives an overview of one possible model; it is possible that a different model will end up being better.) In every thread:

  • At the start of a bytecode, we start a "transaction". This means setting up a thread-local data structure to record a log of what occurs in the transaction.
  • We record in the log all objects that are read, as well as the modifications that we would like to make.
  • During this time, we detect "read" inconsistencies, shown by the object's "last-modified" timestamp being later than the start time of the current transaction, and abort. This prevents the rest of the code from running with inconsistent values.
  • If we reach the end of the bytecode without a "read" inconsistency, then we atomically check for "write" inconsistencies. These are inconsistencies which arise from concurrent updates to objects in the other threads --- either our "write" objects, or our "read" objects.
  • If no inconsistency is found, we "commit" the transaction by copying the delayed writes from the log into main memory.

The points at which a transaction starts or ends are exactly the points at which, in CPython, the Global Interpreter Lock is respectively acquired and released. If we ignore the fact that (purely for performance) CPython acquires and releases the GIL only every N bytecodes, then this means:

  1. Before any bytecode we acquire the GIL (start a transaction), and after the bytecode we release it (ends the transaction); and
  2. Before doing an external call to the C library or the OS we release the GIL (ends the transaction) and afterwards re-acquire it (start the next transaction).
So in particular this model is well suited to the STM condition that we cannot do anything in a transaction that cannot be rolled back, like --- precisely --- system calls. Indeed, by construction, these system calls occur outside a transaction, because in CPython they occur with the GIL released.


A large number of implementation details are still open for now. From a user's point of view (i.e. the programmer using Python), the most relevant one is the overall performance impact. We cannot give precise numbers so far, and we expect the initial performance to be abysmally bad (maybe 10x slower); however, with successive improvements to the locking mechanism, to the global program transformation inserting the locks, to the garbage collector (GC), and to the Just-in-Time (JIT) compiler, we believe that it should be possible to get a roughly reasonable performance (up to maybe 2x slower). For example, the GC can maintain flags on the objects to know that they did not escape their creation thread, and do not need any logging; and the JIT compiler can aggregate several reads or writes to an object into one. We believe that these are the kind of optimizations that can give back a lot of the performance lost.

The state of STM

Transactional Memory is itself a relatively old idea, originating from a 1986 paper by Tom Knight. At first based on hardware support, the idea of software-only transactional memory (STM) was popularized in 1995 and has recently been the focus of intense research.

The approach outlined above --- using STM to form the core of the implementation of a language --- is new, as far as we know. So far, most implementations provide STM as a library feature. It requires explicit usage, often in the form of explicitly declaring which objects must be protected by STM (object-based STMs). It is only recently that native STM support has started to appear, notably in the Clojure language.

STM is described on Wikipedia as an approach that "greatly simplifies conceptual understanding of multithreaded programs and helps make programs more maintainable by working in harmony with existing high-level abstractions such as objects and modules." We actually think that these benefits are important enough to warrant being exposed to the Python programmer as well, instead of being used only internally. This would give the Python programmer a very simple interface:

with atomic:
    <these operations are executed atomically>

(This is an old idea. Funny how back in 2003 people, including me, thought that this was a hack. Now I'm writing a blog post to say "it was not a hack; it's explicitly using locks that is a hack." I'm buying the idea of composability.)

From a practical point of view, I started looking seriously at the University of Rochester STM (RSTM), a C++ library that has been a focus of --- and a collection of results from --- recent research. One particularly representative paper is A Comprehensive Strategy for Contention Management in Software Transactional Memory by Michael F. Spear, Luke Dalessandro, Virendra J. Marathe and Michael L. Scott.


Taking these ideas and applying them in the context of an implementation of a complex high-level language like Python comes with its own challanges. In this context, using PyPy makes sense as both an experimentation platform and as a platform that is recently gaining attention for its performance. The alternatives are unattractive: doing it in CPython for example would mean globally rewriting the interpreter. In PyPy instead, we write it as a transformation that is applied systematically at translation-time. Also, PyPy is a general platform for generating fast interpreters for dynamic languages; the STM implementation in PyPy would work out of the box for other language implementations as well, instead of just for Python.


  • This is mostly me (Armin Rigo) ranting aloud and trying experiments; this post should not be confused as meaning that the whole PyPy team will now spend the next years working on it full-time. As I said it is orthogonal to the actual Python interpreter, and it is in any case a feature that can be turned on or off during translation; I know that in many or most use cases, people are more interested in getting a fast PyPy rather than one which is twice as slow but scales well.
  • Nothing I said is really new. For proof, see Riley and Zilles (2006) as well as Tabba (2010) who both experimented with Hardware Transactional Memory, turning CPython or PyPy interpreter's GIL into start/end transactions, as I describe here.

Thursday, August 18, 2011

PyPy 1.6 - kickass panda

We're pleased to announce the 1.6 release of PyPy. This release brings a lot of bugfixes and performance improvements over 1.5, and improves support for Windows 32bit and OS X 64bit. This version fully implements Python 2.7.1 and has beta level support for loading CPython C extensions. You can download it here:


What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7.1. It's fast (pypy 1.6 and cpython 2.6.2 performance comparison) due to its integrated tracing JIT compiler.

This release supports x86 machines running Linux 32/64 or Mac OS X. Windows 32 is beta (it roughly works but a lot of small issues have not been fixed so far). Windows 64 is not yet supported.

The main topics of this release are speed and stability: on average on our benchmark suite, PyPy 1.6 is between 20% and 30% faster than PyPy 1.5, which was already much faster than CPython on our set of benchmarks.

The speed improvements have been made possible by optimizing many of the layers which compose PyPy. In particular, we improved: the Garbage Collector, the JIT warmup time, the optimizations performed by the JIT, the quality of the generated machine code and the implementation of our Python interpreter.


  • Numerous performance improvements, overall giving considerable speedups:
    • better GC behavior when dealing with very large objects and arrays
    • fast ctypes: now calls to ctypes functions are seen and optimized by the JIT, and they are up to 60 times faster than PyPy 1.5 and 10 times faster than CPython
    • improved generators(1): simple generators now are inlined into the caller loop, making performance up to 3.5 times faster than PyPy 1.5.
    • improved generators(2): thanks to other optimizations, even generators that are not inlined are between 10% and 20% faster than PyPy 1.5.
    • faster warmup time for the JIT
    • JIT support for single floats (e.g., for array('f'))
    • optimized dictionaries: the internal representation of dictionaries is now dynamically selected depending on the type of stored objects, resulting in faster code and smaller memory footprint. For example, dictionaries whose keys are all strings, or all integers. Other dictionaries are also smaller due to bugfixes.
  • JitViewer: this is the first official release which includes the JitViewer, a web-based tool which helps you to see which parts of your Python code have been compiled by the JIT, down until the assembler. The jitviewer 0.1 has already been release and works well with PyPy 1.6.
  • The CPython extension module API has been improved and now supports many more extensions. For information on which one are supported, please refer to our compatibility wiki.
  • Multibyte encoding support: this was of of the last areas in which we were still behind CPython, but now we fully support them.
  • Preliminary support for NumPy: this release includes a preview of a very fast NumPy module integrated with the PyPy JIT. Unfortunately, this does not mean that you can expect to take an existing NumPy program and run it on PyPy, because the module is still unfinished and supports only some of the numpy API. However, barring some details, what works should be blazingly fast :-)
  • Bugfixes: since the 1.5 release we fixed 53 bugs in our bug tracker, not counting the numerous bugs that were found and reported through other channels than the bug tracker.


Hakan Ardo, Carl Friedrich Bolz, Laura Creighton, Antonio Cuni, Maciej Fijalkowski, Amaury Forgeot d'Arc, Alex Gaynor, Armin Rigo and the PyPy team

Friday, August 12, 2011

Visualization of JITted code


We're proud to announce the first public release of the jitviewer. As of now, jitviewer is a slightly internal tool that helps understanding how your Python source code is compiled by the PyPy's JIT all the way down to machine code.

To install it, you need a very recent version of PyPy (newer than 9th of August), for example one of the nightly builds:

  • install pip and distribute either by creating a PyPy virtualenv or by following the installation instructions.
  • make sure to have a source code checkout of PyPy and put it in your PYTHONPATH.
  • pip install jitviewer. Note that you need to run the pip executable which belongs to PyPy, not the globally installed one.

Have a look at the README for how to start it, or try the online demo if you just want to play with it.

The jitviewer is a web application written with flask and jinja2. If you have experience with web development and you want to help PyPy, don't hesitate to contact us, there are plenty of things to improve in it :-).

What does the jitviewer really do?

At the top of the page, you will see the list of pieces of code which has been compiled by the JIT. You will see entries for both normal loops and for "entry bridges". This is not the right place to discuss the difference between those, but you most probably want to look at loops, because usually it's where most of the time is spent.

Note that for each loop, you will see the name of the function which contains the first instruction of the loop. However, thanks to the inlining done by the JIT, it will contain also the code for other functions.

Once you select a loop, the jitviewer shows how the JIT has compiled the Python source code into assembler in a hierarchical way. It displays four levels:

  • Python source code: only the lines shown in azure have been compiled for this particular loop, the ones in gray have not.

  • Python bytecode, the one you would get by doing:

    def f(a, b):
       return a + b
    import dis

    The opcodes are e.g. LOAD_FAST, LOAD_GLOBAL etc. The opcodes which are not in bold have been completely optimized aways by the JIT.

  • Intermediate representation of jit code (IR). This is a combination of operations (like integer addition, reading fields out of structures) and guards (which check that the assumptions we made are actually true). Guards are in red. These operations are "at the same level as C": so, for example, + takes two unboxed integers which can be stored into the register of the CPU.

  • Assembler: you can see it by clicking on "Show assembler" in the menu on the right.

Sometimes you'll find that a guard fails often enough that a new piece of assembler is required to be compiled. This is an alternative path through the code and it's called a bridge. You can see bridges in the jitviewer when there is a link next to a guard. For more information about purpose look up the jit documentation.

I'm still confused

Jitviewer is not perfect when it comes to explaining what's going on. Feel free to pop up on IRC or send us a mail to the mailing list, we'll try to explain and/or improve the situation. Consult the contact page for details.

fijal & antocuni

Tuesday, August 2, 2011

PyPy is faster than C, again: string formatting

String formatting is probably something you do just about every day in Python, and never think about. It's so easy, just "%d %d" % (i, i) and you're done. No thinking about how to size your result buffer, whether your output has an appropriate NULL byte at the end, or any other details. A C equivalent might be:

char x[44];
sprintf(x, "%d %d", i, i);

Note that we had to stop for a second and consider how big numbers might get and overestimate the size (44 = length of the biggest number on 64bit (20) + 1 for the sign * 2 + 1 (for the space) + 1 (NUL byte)), it took the authors of this post, fijal and alex, 3 tries to get the math right on this :-)

This is fine, except you can't even return x from this function, a more fair comparison might be:

char *x = malloc(44 * sizeof(char));
sprintf(x, "%d %d", i, i);

x is slightly overallocated in some situations, but that's fine.

But we're not here to just discuss the implementation of string formatting, we're here to discuss how blazing fast PyPy is at it, with the new unroll-if-alt branch. Given the Python code:

def main():
    for i in xrange(10000000):
        "%d %d" % (i, i)


and the C code:

#include <stdio.h>
#include <stdlib.h>

int main() {
    int i = 0;
    char x[44];
    for (i = 0; i < 10000000; i++) {
        sprintf(x, "%d %d", i, i);

Run under PyPy, at the head of the unroll-if-alt branch, and compiled with GCC 4.5.2 at -O4 (other optimization levels were tested, this produced the best performance). It took 0.85 seconds to execute under PyPy, and 1.63 seconds with the compiled binary. We think this demonstrates the incredible potential of dynamic compilation, GCC is unable to inline or unroll the sprintf call, because it sits inside of libc.

Benchmarking the C code:

#include <stdio.h>
#include <stdlib.h>

int main() {
    int i = 0;
    for (i = 0; i < 10000000; i++) {
        char *x = malloc(44 * sizeof(char));
        sprintf(x, "%d %d", i, i);

Which as discussed above, is more comperable to the Python, gives a result of 1.96 seconds.

Summary of performance:

Platform GCC (stack) GCC (malloc) CPython PyPy (unroll-if-alt)
Time 1.63s 1.96s 10.2s 0.85s
relative to C 1x 0.83x 0.16x 1.9x

Overall PyPy is almost 2x faster. This is clearly win for dynamic compilation over static - the sprintf function lives in libc and so cannot be specializing over the constant string, which has to be parsed every time it's executed. In the case of PyPy, we specialize the assembler if we detect the left hand string of the modulo operator to be constant.

alex & fijal