Wednesday, June 29, 2011

Global Interpreter Lock, or how to kill it

People that listened to my (Armin Rigo) lightning talk at EuroPython know that suddenly, we have a plan to remove the Global Interpreter Lock --- the infamous GIL, the thing in CPython that prevents multiple threads from actually running in your Python code in parallel.

That's not actually new, because Jython has been doing it all along. Jython works by very carefully adding locks to all the mutable built-in types, and by relying on the underlying Java platform to be efficient about them (so that the result is faster than, say, very carefully adding similar locks in CPython). By "very carefully", I mean really really carefully; for example, 'dict1.update(dict2)' needs to lock both dict1 and dict2, but if you do it naively, then a parallel 'dict2.update(dict1)' might cause a deadlock.

All of PyPy, CPython and IronPython have a GIL. But for PyPy we are considering a quite different approach than Jython's, based on Software Transactional Memory. This is a recent development in computer science, and it gives a nicer solution than locking. Here is a short introduction to it.

Say you want to atomically pop an item from 'list1' and append it to 'list2':

def f(list1, list2):
    x = list1.pop()

This is not safe in multithreaded cases (even with the GIL). Say that you call f(l1, l2) in thread 1 and f(l2, l1) in thread 2. What you want is that it has no effect at all (x is moved from one list to the other, then back). But what can occur is that instead the top of the two lists are swapped, depending on timing issues.

One way to fix it is with a global lock:

def f(list1, list2):
    x = list1.pop()

A finer way to fix it is with locks that come with the lists:

def f(list1, list2):
    acquire_all_locks(list1.lock, list2.lock)
    x = list1.pop()
    release_all_locks(list1.lock, list2.lock)

The second solution is a model for Jython's, while the first is a model for CPython's. Indeed, in CPython's interpreter, we acquire the GIL, then we do one bytecode (or actually a number of them, like 100), then we release the GIL; and then we proceed to the next bunch of 100.

Software Transactional Memory (STM) gives a third solution:

def f(list1, list2):
    while True:
        t = transaction()
        x = list1.pop(t)
        list2.append(t, x)
        if t.commit():

In this solution, we make a transaction object and use it in all reads and writes we do to the lists. There are actually several different models, but let's focus on one of them. During a transaction, we don't actually change the global memory at all. Instead, we use the thread-local transaction object. We store in it which objects we read from, which objects we write to, and what values we write. It is only when the transaction reaches its end that we attempt to "commit" it. Committing might fail if other commits have occurred in between, creating inconsistencies; in that case, the transaction aborts and must restart from the beginning.

In the same way as the previous two solutions are models for CPython and Jython, the STM solution looks like it could be a model for PyPy in the future. In such a PyPy, the interpreter would start a transaction, do one or several bytecodes, and then end the transaction; and repeat. This is very similar to what is going on in CPython with the GIL. In particular, it means that it gives programmers all the same guarantees as the GIL does. The only difference is that it can actually run multiple threads in parallel, as long as their code does not interfere with each other. (In particular, if you need not just the GIL but actual locks in your existing multi-threaded program, then this will not magically remove the need for them. You might get an additional built-in module that exposes STM to your Python programs, if you prefer it over locks, but that's another question.)

Why not apply that idea to CPython? Because we would need to change everything everywhere. In the example above, you may have noted that I no longer call 'list1.pop()', but 'list1.pop(t)'; this is a way to tell that the implementation of all the methods needs to be changed, in order to do their work "transactionally". This means that instead of really changing the global memory in which the list is stored, it must instead record the change in the transation object. If our interpreter is written in C, as CPython is, then we need to write it explicitly everywhere. If it is written instead in a higher-level language, as PyPy is, then we can add this behavior as as set of translation rules, and apply them automatically wherever it is necessary. Moreover, it can be a translation-time option: you can either get the current "pypy" with a GIL, or a version with STM, which would be slower due to the extra bookkeeping. (How much slower? I have no clue, but as a wild guess, maybe between 2 and 5 times slower. That is fine if you have enough cores, as long as it scales nicely :-)

A final note: as STM research is very recent (it started around 2003), there are a number of variants around, and it's not clear yet which one is better in which cases. As far as I can tell, the approach described in "A Comprehensive Strategy for Contention Management in Software Transactional Memory" seems to be one possible state-of-the-art; it also seems to be "good enough for all cases".

So, when will it be done? I cannot say yet. It is still at the idea stage, but I think that it can work. How long would it take us to write it? Again no clue, but we are looking at many months rather than many days. This is the sort of thing that I would like to be able to work on full time after the Eurostars funding runs out on September 1. We are currently looking at ways to use crowdfunding to raise money so that I can do exactly that. Expect a blog post about that very soon. But this looks like a perfect candidate for crowdfunding -- there are at least thousands of you who would be willing to pay 10s of Euros to Kill the GIL. Now we only have to make this happen.

Wednesday, June 8, 2011

Report back from our survey

Hi all,

I'm here to report back the results of our survey. First, we're very pleased to report that a number of you guys are happilly running PyPy in production! Most (97%) of the respondants using PyPy are using it because it's faster, but a further 26% (respondants could choose multiple answers) are using it because of lower memory usage. Of users who aren't using PyPy, the most common reason was C extensions, followed by "Other".

From reading the extra comments section there are a few things we've learned:

  1. Google docs needs a better UI for this stuff
  2. A huge number of people want NumPy and SciPy, it was easily the most requested C extension (25% of respondants said somthing about NumPy). We've already blogged on the topic of our plans for NumPy.
  3. Having packages in the various OS's repositories would be a big help in getting users up and running.

A huge thanks to everyone who responded! Finally, if you're using PyPy in production we'd love to get a testimonial from you, if you're willing to spare a few minutes to give us a quote or two please get in contact with us via our mailing list.

Thanks, Alex