# 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 side-stepping what is known in the Python world as the "global interpreter lock (GIL)" problem.

"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 mostly 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.

## Introduction

pypy-stm is a variant of the regular PyPy interpreter. With caveats listed below, it should be in theory within 25%-50% slower than a regular PyPy, comparing the JIT version in both cases. It is called STM for Software Transactional Memory, which is the internal technique used (see Reference to implementation details).

What you get in exchange for this slow-down is that pypy-stm runs any multithreaded Python program on multiple CPUs at once. Programs running two threads or more in parallel should ideally run faster than in a regular PyPy, either now or soon as issues are fixed. In one way, that's all there is to it: this is a GIL-less Python, feel free to download and try it. However, the deeper idea behind the pypy-stm project is to improve what is so far the state-of-the-art for using multiple CPUs, which for cases where separate processes don't work is done by writing explicitly multi-threaded programs. Instead, pypy-stm is pushing forward an approach to hide the threads, as described below in atomic sections.

## Current status

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 12.04 binary here (pypy-2.2.x-stm*.tar.bz2; this version is a release mode, but not stripped of debug symbols). The current version supports four "segments", which means that it will run up to four threads in parallel, in other words it is running a thread pool up to 4 threads emulating normal threads.

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 ... for all checkouts). Then apply all the patches in this directory: they are fixes for the very extensive usage that pypy-stm does of a clang-only feature (without them, you get crashes of clang). Then get the branch stmgc-c7 of PyPy and run:

rpython/bin/rpython -Ojit --stm pypy/goal/targetpypystandalone.py


Caveats:

• So far, small examples work fine, but there are still a number of bugs. We're busy fixing them.
• Currently limited to 1.5 GB of RAM (this is just a parameter in core.h). Memory overflows are not detected correctly, so may cause segmentation faults.
• The JIT warm-up time is abysmal (as opposed to the regular PyPy's, which is "only" bad). Moreover, you should run it with a command like pypy-stm --jit trace_limit=60000 args...; the default value of 6000 for trace_limit is currently too low (6000 should become reasonable again as we improve). Also, in order to produce machine code, the JIT needs to enter a special single-threaded mode for now. This all means that you will get very bad performance results if your program doesn't run for many seconds for now.
• 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, as well as programs that modify large lists or dicts, 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 Statistics below).
• You can use atomic sections, but the most visible missing thing is that you don't get reports about the "conflicts" you get. This would be the first thing that you need in order to start using atomic sections more extensively. Also, for now: for better results, try to explicitly force a transaction break just before (and possibly after) each large atomic section, with time.sleep(0).
• Forking the process is slow because the complete memory needs to be copied manually right now.
• Very long-running processes should eventually crash on an assertion error because of a non-implemented overflow of an internal 29-bit number, but this requires at the very least ten hours --- more probably, several days or more.

## Statistics

When a non-main thread finishes, you get 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
bookkeeping                  51418  0.606 s
minor gc                    162970  1.135 s
major gc                         1  0.019 s
sync pause                   59173  1.738 s
spin loop                   129512  0.094 s


The first number is a counter; 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 a bit larger than that; to investigate). Everything else is overhead of various forms. (Short-, medium- and long-term future work involves reducing this overhead :-)

These statistics are not printed out for the main thread, for now.

## Atomic sections

While one of the goal of pypy-stm is to give a GIL-free but otherwise unmodified Python, the other goal is to push for a better way to use multithreading. For this, you (as the Python programmer) get an API in the __pypy__.thread submodule:

• __pypy__.thread.atomic: a context manager (i.e. you use it in a with __pypy__.thread.atomic: statement). It runs the whole block of code without breaking the current transaction --- from the point of view of a regular CPython/PyPy, this is equivalent to saying that the GIL will not be released at all between the start and the end of this block of code.

The obvious usage is to use atomic blocks in the same way as one would use locks: to protect changes to some shared data, you do them in a with atomic block, just like you would otherwise do them in a with mylock block after mylock = thread.allocate_lock(). This allows you not to care about acquiring the correct locks in the correct order; it is equivalent to having only one global lock. This is how transactional memory is generally described: as a way to efficiently execute such atomic blocks, running them in parallel while giving the illusion that they run in some serial order.

However, the less obvious intended usage of atomic sections is as a wide-ranging replacement of explicit threads. You can turn a program that is not multi-threaded at all into a program that uses threads internally, together with large atomic sections to keep the behavior unchanged. 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, see transaction.py in lib_pypy. The idea is that if you have a program where the function f(key, value) runs on every item of some big dictionary, you can replace the loop with:

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


This code runs the various calls to f(key, value) using a thread pool, but every single call is done in an atomic section. The end result is that the behavior should be exactly equivalent: you don't get any extra multithreading issue.

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.

Other APIs in pypy-stm:

• __pypy__.thread.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.)
• __pypy__.thread.exclusive_atomic: same as atomic, but raises an exception if you attempt to nest it inside another atomic.
• __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.

Note that all of this API is (or will be) implemented in a regular PyPy too: for example, with atomic will simply mean "don't release the GIL" and getsegmentlimit() will return 1.

## 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 assembler.py).