- Software Transactional Memory
- What pypy-stm is for
- Getting Started
- Python 3, CPython, and others
- User Guide
- See also
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
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, CPython,
and others.) 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-stmis 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-stmprovides (but does not impose) a special API to the user in the pure Python module
transaction. This module is based on the lower-level module
pypystm, but also provides some compatibily with non-STM PyPy’s or CPython’s.
- Building on top of the way the GIL is removed, we will talk about How to write multithreaded programs: the 10‘000-feet view and transaction.TransactionQueue.
pypy-stm gives a Python without the GIL. This means that it is
useful in situations where the GIL is the problem in the first place.
(This includes cases where the program can easily be modified to run
in multiple threads; often, we don’t consider doing that precisely
because of the GIL.)
However, there are plenty of cases where the GIL is not the problem.
Do not hope
pypy-stm to be helpful in these cases! This includes
all programs that use multiple threads but don’t actually spend a lot
of time running Python code. For example, it may be spending all its
time waiting for I/O to occur, or performing some long computation on
a huge matrix. These are cases where the CPU is either idle, or in
some C/Fortran library anyway; in both cases, the interpreter (either
CPython or the regular PyPy) should release the GIL around the
external calls. The threads will thus not end up fighting for the
pypy-stm requires 64-bit Linux for now.
Development is done in the branch stmgc-c8. If you are only interested in trying it out, please pester us until we upload a recent prebuilt binary. 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 gcc(!). See the instructions here: https://bitbucket.org/pypy/stmgc/src/default/gcc-seg-gs/ (Note that these patches are being incorporated into gcc. It is likely that future versions of gcc will not need to be patched any more.)
Then get the branch stmgc-c8 of PyPy and run:
cd pypy/goal ../../rpython/bin/rpython -Ojit --stm
At the end, this will try to compile the generated C code by calling
gcc-seg-gs, which must be the script you installed in the
THIS PAGE IS OLD, THE REST IS ABOUT STMGC-C7 WHEREAS THE CURRENT DEVELOPMENT WORK IS DONE ON STMGC-C8
- NEW: It seems to work fine, without crashing any more. Please report any crash you find (or other 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 “translate.py”– which we are still trying to understand. One suspect is our partial GC implementation, see below.
- NEW: the
PYPYSTMenvironment variable and the
pypy/stm/print_stm_log.pyscript let you know exactly which “conflicts” occurred. This is described in the section transaction.TransactionQueue below.
- NEW: special transaction-friendly APIs (like
stmdict), described in the section transaction.TransactionQueue below. The old API changed again, mostly moving to different modules. Sorry about that. I feel it’s a better idea to change the API early instead of being stuck with a bad one later...
- Currently limited to 1.5 GB of RAM (this is just a parameter in core.h – theoretically. In practice, increase it too much and clang crashes again). Memory overflows are not correctly handled; they cause segfaults.
- NEW: The JIT warm-up time improved again, but is still relatively large. In order to produce machine code, the JIT needs to enter “inevitable” mode. 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.
- 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 bleeding
stmgc-c8is better at that.)
- Weakrefs might appear to work a bit strangely for now, sometimes
staying alive throught
gc.collect(), or even dying but then un-dying for a short time before dying again. A similar problem can show up occasionally elsewhere with accesses to some external resources, where the (apparent) serialized order doesn’t match the underlying (multithreading) order. These are bugs (partially fixed already in
stmgc-c8). Also, debugging helpers like
weakref.getweakrefcount()might give wrong answers.
- 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).
- 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 28-bit counter.
- The recursion detection code was not reimplemented. Infinite recursion just segfaults for now.
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. But in general, all the tedious transformations are done by RPython and you’re only left with the (hopefully few) hard and interesting bits.
The core of STM works as a library written in C (see reference to
implementation details below). It means that it can be used on
other interpreters than the ones produced by RPython. Duhton is an
early example of that. At this point, you might think about adapting
this library for CPython. You’re warned, though: as far as I can
tell, it is a doomed idea. I had a hard time debugging Duhton, and
that’s infinitely simpler than CPython. Even ignoring that, you can
see in the C sources of Duhton that many core design decisions are
different than in CPython: no refcounting; limited support for
prebuilt “static” objects;
calls everywhere (and getting very rare and very obscure bugs if you
forget one); and so on. You could imagine some custom special-purpose
extension of the C language, which you would preprocess to regular C.
In my opinion that’s starting to look a lot like RPython itself, but
maybe you’d prefer this approach. Of course you still have to worry
about each and every C extension module you need, but maybe you’d have
a way forward.
PyPy-STM offers two ways to write multithreaded programs:
- the traditional way, using the
threadingmodules, described first.
TransactionQueue, described next, as a way to hide the low-level notion of threads.
The issues with low-level threads are well known (particularly in other
languages that don’t have GIL-based interpreters): memory corruption,
deadlocks, livelocks, and so on. There are alternative approaches to
dealing directly with threads, like OpenMP. These approaches
typically enforce some structure on your code.
is in part similar: your program needs to have “some chances” of
parallelization before you can apply it. But I believe that the scope
of applicability is much larger with
TransactionQueue than with
other approaches. It usually works without forcing a complete
reorganization of your existing code, and it works on any Python
program which has got latent and 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 a simple API.
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,
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_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 “transactions”. 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.
In CPU-hungry programs, we can often easily identify outermost loops over some data structure, or other repetitive algorithm, where each “block” consists of processing a non-trivial amount of data, and where the blocks “have a good chance” to be independent from each other. We don’t need to prove that they are actually independent: it is enough if they are often independent — or, more precisely, if we think they should be often independent.
One typical example would look like this, where the function
typically invokes a large amount of code:
for key, value in bigdict.items(): func(key, value)
Then you simply replace the loop with:
from transaction import TransactionQueue tr = TransactionQueue() for key, value in bigdict.items(): tr.add(func, key, value) tr.run()
This code’s behavior is equivalent. Internally, the
TransactionQueue object will start N threads and try to run the
func(key, value) calls on all threads in parallel. But note the
difference with a regular thread-pooling library, as found in many
lower-level languages than Python: the function calls are not randomly
interleaved with each other just because they run in parallel. The
behavior did not change because we are using
All the calls still appear to execute in some serial order.
A typical usage of
TransactionQueue goes like that: at first,
the performance does not increase.
In fact, it is likely to be worse. Typically, this is
indicated by the total CPU usage, which remains low (closer to 1 than
N cores). First note that it is expected that the CPU usage should
not go much higher than 1 in the JIT warm-up phase: you must run a
program for several seconds, or for larger programs at least one
minute, to give the JIT a chance to warm up enough. But if CPU usage
remains low even afterwards, then the
PYPYSTM environment variable
can be used to track what is going on.
Run your program with
PYPYSTM=logfile to produce a log file called
logfile. Afterwards, use the
utility to inspect the content of this log file. It produces output
like this (sorted by amount of time lost, largest first):
10.5s lost in aborts, 1.25s paused (12412x STM_CONTENTION_WRITE_WRITE) File "foo.py", line 10, in f someobj.stuff = 5 File "bar.py", line 20, in g someobj.other = 10
This means that 10.5 seconds were lost running transactions that were
aborted (which caused another 1.25 seconds of lost time by pausing),
because of the reason shown in the two independent single-entry
tracebacks: one thread ran the line
someobj.stuff = 5, whereas
another thread concurrently ran the line
someobj.other = 10 on the
same object. These two writes are done to the same object. This
causes a conflict, which aborts one of the two transactions. In the
example above this occurred 12412 times.
The two other conflict sources are
which means that two transactions both tried to do an external
operation, like printing or reading from a socket or accessing an
external array of raw data; and
means that one transaction wrote to an object but the other one merely
read it, not wrote to it (in that case only the writing transaction is
reported; the location for the reads is not recorded because doing so
is not possible without a very large performance impact).
Common causes of conflicts:
First of all, any I/O or raw manipulation of memory turns the transaction inevitable (“must not abort”). There can be only one inevitable transaction running at any time. A common case is if each transaction starts with sending data to a log file. You should refactor this case so that it occurs either near the end of the transaction (which can then mostly run in non-inevitable mode), or delegate it to a separate transaction or even a separate thread.
Writing to a list or a dictionary conflicts with any read from the same list or dictionary, even one done with a different key. For dictionaries and sets, you can try the types
transaction.stmset, which behave mostly like
setbut allow concurrent access to different keys. (What is missing from them so far is lazy iteration: for example,
stmdict.iterkeys()is implemented as
iter(stmdict.keys()); and, unlike PyPy’s dictionaries and sets, the STM versions are not ordered.) There are also experimental
stmidsetclasses using the identity of the key.
time.clock()turn the transaction inevitable in order to guarantee that a call that appears to be later will really return a higher number. If getting slightly unordered results is fine, use
transaction.clock(). The latter operations guarantee to return increasing results only if you can “prove” that two calls occurred in a specific order (for example because they are both called by the same thread). In cases where no such proof is possible, you might get randomly interleaved values. (If you have two independent transactions, they normally behave as if one of them was fully executed before the other; but using
transaction.time()you might see the “hidden truth” that they are actually interleaved.)
transaction.threadlocalpropertycan be used at class-level:
class Foo(object): # must be a new-style class! x = transaction.threadlocalproperty() y = transaction.threadlocalproperty(dict)
This declares that instances of
Foohave two attributes
ythat are thread-local: reading or writing them from concurrently-running transactions will return independent results. (Any other attributes of
Fooinstances will be globally visible from all threads, as usual.) This is useful together with
TransactionQueuefor these two cases:
- For attributes of long-lived objects that change during one transaction, but should always be reset to some initial value around transaction (for example, initialized to 0 at the start of a transaction; or, if used for a list of pending things to do within this transaction, it will always be empty at the end of one transaction).
- For general caches across transactions. With
TransactionQueueyou get a pool of a fixed number N of threads, each running the transactions serially. A thread-local property will have the value last stored in it by the same thread, which may come from a random previous transaction. Basically, you get N copies of the property’s value, and each transaction accesses a random copy. It works fine for caches.
In more details, the optional argument to
threadlocalproperty()is the default value factory: in case no value was assigned in the current thread yet, the factory is called and its result becomes the value in that thread (like
collections.defaultdict). If no default value factory is specified, uninitialized reads raise
In addition to all of the above, there are cases where write-write conflicts are caused by writing the same value to an attribute again and again. See for example ea2e519614ab: this fixes two such issues where we write an object field without first checking if we already did it. The
dont_change_any_morefield is a flag set to
Truein that part of the code, but usually this
rtyper_makekey()method will be called many times for the same object; the code used to repeatedly set the flag to
True, but now it first checks and only does the write if it is
False. Similarly, in the second half of the checkin, the method
setup_block_entry()used to both assign the
concretetypefields and return a list, but its two callers were different: one would really need the
concretetypefields initialized, whereas the other would only need to get its result list — the
concretetypefield in that case might already be set or not, but that would not matter.
Note that Python is a complicated language; there are a number of less common cases that may cause conflict (of any kind) where we might not expect it at priori. In many of these cases it could be fixed; please report any case that you don’t understand.
TransactionQueue class described above is based on atomic
sections, which are blocks of code which you want to execute without
“releasing the GIL”. In STM terms, this means blocks of code that are
executed while guaranteeing that the transaction is not interrupted in
the middle. This is experimental and may be removed in the future
if Software lock elision is ever implemented.
Here is a direct usage example:
with transaction.atomic: assert len(lst1) == 10 x = lst1.pop(0) lst1.append(x)
In this 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
x in lst1 without any
particular synchronization, and always see the same results,
True. It will never see the intermediate
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: time.sleep(10)
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
Note that if you want to experiment with
atomic, you may have to
manually add 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 may be bigger. Therefore, if you run a big atomic block, it
is a good idea to break the transaction just before. This can be done
transaction.hint_commit_soon(). (This may be fixed at
There are also issues with the interaction of regular locks and atomic
blocks. This can be seen if you write to files (which have locks),
including with a
thread.error. (Don’t rely on it; it may also deadlock.)
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 and may be the
reason for atomic block support to eventually be removed.)
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
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
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.
- First, note that the
transactionmodule is found in the file
lib_pypy/transaction.py. This file can be copied around to execute the same programs on CPython or on non-STM PyPy, with fall-back behavior. (One case where the behavior differs is
atomic, which is in this fall-back case just a regular lock; so
with atomiconly prevent other threads from entering other
with atomicsections, but won’t prevent other threads from running non-atomic code.)
transaction.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.
__pypy__.thread.signals_enabled: a context manager that runs its block of code 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.
pypystm.exclusive_atomic: a context manager similar to
transaction.atomicbut which complains if it is nested.
transaction.is_atomic(): return True if called from an atomic context.
pypystm.count(): return a different positive integer every time it is called. This works without generating conflicts. The returned integers are only roughly in increasing order; this should not be relied upon.
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
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
However, 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. (You can work around it with
transaction.stmdict, but for that specific example, some STM-aware
queue should eventually be designed.)
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
generally because 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
(This is not unrelated 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 lock elision eventually replaces atomic sections, we wouldn’t get long-running inevitable transactions, but the same problem occurs in a different way: doing I/O cancels lock elision, and the lock turns into a real lock. This prevents 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
STMGC-C7 is described in detail in a technical report.
A separate position paper gives an overview of our position about STM in general.
The core of the implementation is in a separate C library called stmgc, in the c7 subdirectory (current version of pypy-stm) and in the c8 subdirectory (bleeding edge version). 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 assembler.py).
See also https://bitbucket.org/pypy/pypy/raw/default/pypy/doc/project-ideas.rst (section about STM).