Software Transactional Memory

This page is about pypy-stm, a special in-development version of PyPy which can run multiple independent CPU-hungry threads in the same process in parallel. It is a solution to what is known in the Python world as the “global interpreter lock (GIL)” problem — it is an implementation of Python without the GIL.

“STM” stands for Software Transactional Memory, the technique used internally. This page describes pypy-stm from the perspective of a user, describes work in progress, and finally gives references to more implementation details.

This work was done by Remi Meier and Armin Rigo. Thanks to all donors for crowd-funding the work so far! Please have a look at the 2nd call for donation.


pypy-stm is a variant of the regular PyPy interpreter. (This version supports Python 2.7; see below for Python 3.) With caveats listed below, it should be in theory within 20%-50% slower than a regular PyPy, comparing the JIT version in both cases (but see below!). It is called STM for Software Transactional Memory, which is the internal technique used (see Reference to implementation details).

The benefit is that the resulting pypy-stm can execute multiple threads of Python code in parallel. Programs running two threads or more in parallel should ideally run faster than in a regular PyPy (either now, or soon as bugs are fixed).

  • pypy-stm is fully compatible with a GIL-based PyPy; you can use it as a drop-in replacement and multithreaded programs will run on multiple cores.
  • pypy-stm does not impose any special API to the user, but it provides a new pure Python module called transactional_memory with features to inspect the state or debug conflicts that prevent parallelization. This module can also be imported on top of a non-STM PyPy or CPython.
  • Building on top of the way the GIL is removed, we will talk about Atomic sections, Transactions, etc.: a better way to write parallel programs.

Getting Started

pypy-stm requires 64-bit Linux for now.

Development is done in the branch stmgc-c7. If you are only interested in trying it out, you can download a Ubuntu binary here (pypy-stm-2.3*.tar.bz2, Ubuntu 12.04-14.04). The current version supports four “segments”, which means that it will run up to four threads in parallel.

To build a version from sources, you first need to compile a custom version of clang(!); we recommend downloading llvm and clang like described here, but at revision 201645 (use svn co -r 201645 <path> for all checkouts). Then apply all the patches in this directory: they are fixes for a clang-only feature that hasn’t been used so heavily in the past (without the patches, you get crashes of clang). Then get the branch stmgc-c7 of PyPy and run:

rpython/bin/rpython -Ojit --stm pypy/goal/

Current status

  • So far, small examples work fine, but there are still a few bugs. We’re busy fixing them as we find them; feel free to report bugs.
  • It runs with an overhead as low as 20% on examples like “richards”. There are also other examples with higher overheads –currently up to 2x for “”– which we are still trying to understand. One suspect is our partial GC implementation, see below.
  • Currently limited to 1.5 GB of RAM (this is just a parameter in core.h). Memory overflows are not correctly handled; they cause segfaults.
  • The JIT warm-up time improved recently but is still bad. In order to produce machine code, the JIT needs to enter a special single-threaded mode for now. This means that you will get bad performance results if your program doesn’t run for several seconds, where several can mean many. When trying benchmarks, be sure to check that you have reached the warmed state, i.e. the performance is not improving any more. This should be clear from the fact that as long as it’s producing more machine code, pypy-stm will run on a single core.
  • The GC is new; although clearly inspired by PyPy’s regular GC, it misses a number of optimizations for now. Programs allocating large numbers of small objects that don’t immediately die (surely a common situation) suffer from these missing optimizations.
  • The GC has no support for destructors: the __del__ method is never called (including on file objects, which won’t be closed for you). This is of course temporary. Also, weakrefs might appear to work a bit strangely for now (staying alive even though gc.collect(), or even dying but then un-dying for a short time before dying again).
  • The STM system is based on very efficient read/write barriers, which are mostly done (their placement could be improved a bit in JIT-generated machine code). But the overall bookkeeping logic could see more improvements (see Low-level statistics below).
  • Forking the process is slow because the complete memory needs to be copied manually. A warning is printed to this effect.
  • Very long-running processes (on the order of days) will eventually crash on an assertion error because of a non-implemented overflow of an internal 29-bit number.

Python 3

In this document I describe “pypy-stm”, which is based on PyPy’s Python 2.7 interpreter. Supporting Python 3 should take about half an afternoon of work. Obviously, what I don’t mean is that by tomorrow you can have a finished and polished “pypy3-stm” product. General py3k work is still missing; and general stm work is also still missing. But they are rather independent from each other, as usual in PyPy. The required afternoon of work will certainly be done one of these days now that the internal interfaces seem to stabilize.

The same is true for other languages implemented in the RPython framework, although the amount of work to put there might vary, because the STM framework within RPython is currently targeting the PyPy interpreter and other ones might have slightly different needs.

User Guide

Drop-in replacement

Multithreaded, CPU-intensive Python programs should work unchanged on pypy-stm. They will run using multiple CPU cores in parallel.

The existing semantics of the GIL (Global Interpreter Lock) are unchanged: although running on multiple cores in parallel, pypy-stm gives the illusion that threads are run serially, with switches only occurring between bytecodes, not in the middle of them. Programs can rely on this: using shared_list.append()/pop() or shared_dict.setdefault() as synchronization mecanisms continues to work as expected.

This works by internally considering the points where a standard PyPy or CPython would release the GIL, and replacing them with the boundaries of “transaction”. Like their database equivalent, multiple transactions can execute in parallel, but will commit in some serial order. They appear to behave as if they were completely run in this serialization order.

Atomic sections

PyPy supports atomic sections, which are blocks of code which you want to execute without “releasing the GIL”. This is experimental and may be removed in the future. In STM terms, this means blocks of code that are executed while guaranteeing that the transaction is not interrupted in the middle.

Here is a usage example:

with __pypy__.thread.atomic:
    assert len(lst1) == 10
    x = lst1.pop(0)

In this (bad) example, we are sure that the item popped off one end of the list is appened again at the other end atomically. It means that another thread can run len(lst1) or x in lst1 without any particular synchronization, and always see the same results, respectively 10 and True. It will never see the intermediate state where lst1 only contains 9 elements. Atomic sections are similar to re-entrant locks (they can be nested), but additionally they protect against the concurrent execution of any code instead of just code that happens to be protected by the same lock in other threads.

Note that the notion of atomic sections is very strong. If you write code like this:

with __pypy__.thread.atomic:

then, if you think about it as if we had a GIL, you are executing a 10-seconds-long atomic transaction without releasing the GIL at all. This prevents all other threads from progressing at all. While it is not strictly true in pypy-stm, the exact rules for when other threads can progress or not are rather complicated; you have to consider it likely that such a piece of code will eventually block all other threads anyway.

Note that if you want to experiment with atomic, you may have to add manually a transaction break just before the atomic block. This is because the boundaries of the block are not guaranteed to be the boundaries of the transaction: the latter is at least as big as the block, but maybe bigger. Therefore, if you run a big atomic block, it is a good idea to break the transaction just before. This can be done e.g. by the hack of calling time.sleep(0). (This may be fixed at some point.)

There are also issues with the interaction of locks and atomic blocks. This can be seen if you write to files (which have locks), including with a print to standard output. If one thread tries to acquire a lock while running in an atomic block, and another thread has got the same lock, then the former may fail with a thread.error. The reason is that “waiting” for some condition to become true –while running in an atomic block– does not really make sense. For now you can work around it by making sure that, say, all your prints are either in an atomic block or none of them are. (This kind of issue is theoretically hard to solve.)


Not Implemented Yet

The thread module’s locks have their basic semantic unchanged. However, using them (e.g. in with my_lock: blocks) starts an alternative running mode, called Software lock elision. This means that PyPy will try to make sure that the transaction extends until the point where the lock is released, and if it succeeds, then the acquiring and releasing of the lock will be “elided”. This means that in this case, the whole transaction will technically not cause any write into the lock object — it was unacquired before, and is still unacquired after the transaction.

This is specially useful if two threads run with my_lock: blocks with the same lock. If they each run a transaction that is long enough to contain the whole block, then all writes into the lock will be elided and the two transactions will not conflict with each other. As usual, they will be serialized in some order: one of the two will appear to run before the other. Simply, each of them executes an “acquire” followed by a “release” in the same transaction. As explained above, the lock state goes from “unacquired” to “unacquired” and can thus be left unchanged.

This approach can gracefully fail: unlike atomic sections, there is no guarantee that the transaction runs until the end of the block. If you perform any input/output while you hold the lock, the transaction will end as usual just before the input/output operation. If this occurs, then the lock elision mode is cancelled and the lock’s “acquired” state is really written.

Even if the lock is really acquired already, a transaction doesn’t have to wait for it to become free again. It can enter the elision-mode anyway and tentatively execute the content of the block. It is only at the end, when trying to commit, that the thread will pause. As soon as the real value stored in the lock is switched back to “unacquired”, it can then proceed and attempt to commit its already-executed transaction (which can fail and abort and restart from the scratch, as usual).

Note that this is all not implemented yet, but we expect it to work even if you acquire and release several locks. The elision-mode transaction will extend until the first lock you acquired is released, or until the code performs an input/output or a wait operation (for example, waiting for another lock that is currently not free). In the common case of acquiring several locks in nested order, they will all be elided by the same transaction.

Atomic sections, Transactions, etc.: a better way to write parallel programs

(This section is based on locks as we plan to implement them, but also works with the existing atomic sections.)

In the cases where elision works, the block of code can run in parallel with other blocks of code even if they are protected by the same lock. You still get the illusion that the blocks are run sequentially. This works even for multiple threads that run each a series of such blocks and nothing else, protected by one single global lock. This is basically the Python application-level equivalent of what was done with the interpreter in pypy-stm: while you think you are writing thread-unfriendly code because of this global lock, actually the underlying system is able to make it run on multiple cores anyway.

This capability can be hidden in a library or in the framework you use; the end user’s code does not need to be explicitly aware of using threads. For a simple example of this, there is in lib_pypy. The idea is that you write, or already have, some program where the function f(key, value) runs on every item of some big dictionary, say:

for key, value in bigdict.items():
    f(key, value)

Then you simply replace the loop with:

for key, value in bigdict.items():
    transaction.add(f, key, value)

This code runs the various calls to f(key, value) using a thread pool, but every single call is executed under the protection of a unique lock. The end result is that the behavior is exactly equivalent — in fact it makes little sense to do it in this way on a non-STM PyPy or on CPython. But on pypy-stm, the various locked calls to f(key, value) can tentatively be executed in parallel, even if the observable result is as if they were executed in some serial order.

This approach hides the notion of threads from the end programmer, including all the hard multithreading-related issues. This is not the first alternative approach to explicit threads; for example, OpenMP is one. However, it is one of the first ones which does not require the code to be organized in a particular fashion. Instead, it works on any Python program which has got latent, imperfect parallelism. Ideally, it only requires that the end programmer identifies where this parallelism is likely to be found, and communicates it to the system, using for example the transaction.add() scheme.

API of transactional_memory

The new pure Python module transactional_memory runs on both CPython and PyPy, both with and without STM. It contains:

  • getsegmentlimit(): return the number of “segments” in this pypy-stm. This is the limit above which more threads will not be able to execute on more cores. (Right now it is limited to 4 due to inter-segment overhead, but should be increased in the future. It should also be settable, and the default value should depend on the number of actual CPUs.) If STM is not available, this returns 1.
  • print_abort_info(minimum_time=0.0): debugging help. Each thread remembers the longest abort or pause it did because of cross-thread contention. This function prints it to stderr if the time lost is greater than minimum_time seconds. The record is then cleared, to make it ready for new events. This function returns True if it printed a report, and False otherwise.

API of __pypy__.thread

The __pypy__.thread submodule is a built-in module of PyPy that contains a few internal built-in functions used by the transactional_memory module, plus the following:

  • __pypy__.thread.atomic: a context manager to run a block in fully atomic mode, without “releasing the GIL”. (May be eventually removed?)
  • __pypy__.thread.signals_enabled: a context manager that runs its block with signals enabled. By default, signals are only enabled in the main thread; a non-main thread will not receive signals (this is like CPython). Enabling signals in non-main threads is useful for libraries where threads are hidden and the end user is not expecting his code to run elsewhere than in the main thread.


Based on Software Transactional Memory, the pypy-stm solution is prone to “conflicts”. To repeat the basic idea, threads execute their code speculatively, and at known points (e.g. between bytecodes) they coordinate with each other to agree on which order their respective actions should be “committed”, i.e. become globally visible. Each duration of time between two commit-points is called a transaction.

A conflict occurs when there is no consistent ordering. The classical example is if two threads both tried to change the value of the same global variable. In that case, only one of them can be allowed to proceed, and the other one must be either paused or aborted (restarting the transaction). If this occurs too often, parallelization fails.

How much actual parallelization a multithreaded program can see is a bit subtle. Basically, a program not using __pypy__.thread.atomic or eliding locks, or doing so for very short amounts of time, will parallelize almost freely (as long as it’s not some artificial example where, say, all threads try to increase the same global counter and do nothing else).

However, using if the program requires longer transactions, it comes with less obvious rules. The exact details may vary from version to version, too, until they are a bit more stabilized. Here is an overview.

Parallelization works as long as two principles are respected. The first one is that the transactions must not conflict with each other. The most obvious sources of conflicts are threads that all increment a global shared counter, or that all store the result of their computations into the same list — or, more subtly, that all pop() the work to do from the same list, because that is also a mutation of the list. (It is expected that some STM-aware library will eventually be designed to help with conflict problems, like a STM-aware queue.)

A conflict occurs as follows: when a transaction commits (i.e. finishes successfully) it may cause other transactions that are still in progress to abort and retry. This is a waste of CPU time, but even in the worst case senario it is not worse than a GIL, because at least one transaction succeeds (so we get at worst N-1 CPUs doing useless jobs and 1 CPU doing a job that commits successfully).

Conflicts do occur, of course, and it is pointless to try to avoid them all. For example they can be abundant during some warm-up phase. What is important is to keep them rare enough in total.

Another issue is that of avoiding long-running so-called “inevitable” transactions (“inevitable” is taken in the sense of “which cannot be avoided”, i.e. transactions which cannot abort any more). Transactions like that should only occur if you use __pypy__.thread.atomic, generally become of I/O in atomic blocks. They work, but the transaction is turned inevitable before the I/O is performed. For all the remaining execution time of the atomic block, they will impede parallel work. The best is to organize the code so that such operations are done completely outside __pypy__.thread.atomic.

(This is related to the fact that blocking I/O operations are discouraged with Twisted, and if you really need them, you should do them on their own separate thread.)

In case of lock elision, we don’t get long-running inevitable transactions, but a different problem can occur: doing I/O cancels lock elision, and the lock turns into a real lock, preventing other threads from committing if they also need this lock. (More about it when lock elision is implemented and tested.)


XXX this section mostly empty for now

Low-level statistics

When a non-main thread finishes, you get low-level statistics printed to stderr, looking like that:

thread 0x7f73377fe600:
    outside transaction          42182    0.506 s
    run current                  85466    0.000 s
    run committed                34262    3.178 s
    run aborted write write       6982    0.083 s
    run aborted write read         550    0.005 s
    run aborted inevitable         388    0.010 s
    run aborted other                0    0.000 s
    wait free segment                0    0.000 s
    wait write read                 78    0.027 s
    wait inevitable                887    0.490 s
    wait other                       0    0.000 s
    sync commit soon                 1    0.000 s
    bookkeeping                  51418    0.606 s
    minor gc                    162970    1.135 s
    major gc                         1    0.019 s
    sync pause                   59173    1.738 s
    longest recordered marker          0.000826 s
    "File "", line 5, in f"

On each line, the first number is a counter, and the second number gives the associated time — the amount of real time that the thread was in this state. The sum of all the times should be equal to the total time between the thread’s start and the thread’s end. The most important points are “run committed”, which gives the amount of useful work, and “outside transaction”, which should give the time spent e.g. in library calls (right now it seems to be larger than that; to investigate). The various “run aborted” and “wait” entries are time lost due to conflicts. Everything else is overhead of various forms. (Short-, medium- and long-term future work involves reducing this overhead :-)

The last two lines are special; they are an internal marker read by transactional_memory.print_abort_info().

Reference to implementation details

The core of the implementation is in a separate C library called stmgc, in the c7 subdirectory. Please see the README.txt for more information. In particular, the notion of segment is discussed there.

PyPy itself adds on top of it the automatic placement of read and write barriers and of “becomes-inevitable-now” barriers, the logic to start/stop transactions as an RPython transformation and as supporting C code, and the support in the JIT (mostly as a transformation step on the trace and generation of custom assembler in