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Thought this was cool: PyPy Status Blog: Multicore Programming in PyPy and CPython

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Comments: “PyPy Status Blog: Multicore Programming in PyPy and CPython”

URL: http://morepypy.blogspot.de/2012/08/multicore-programming-in-pypy-and.html

Hi all,

This is a short “position paper” kind of post about my view (Armin
Rigo’s) on the future of multicore programming in high-level languages.
It is a summary of the
keynote presentation at EuroPython. As I learned by talking with people
afterwards, I am not a good enough speaker to manage to convey a deeper
message in a 20-minutes talk. I will try instead to convey it in a
250-lines post…

This is about three points:

We often hear about people wanting a version of Python running without
the Global Interpreter Lock (GIL): a “GIL-less Python”. But what we
programmers really need is not just a GIL-less Python — we need a
higher-level way to write multithreaded programs than using directly
threads and locks. One way is Automatic Mutual Exclusion (AME), which
would give us an “AME Python”.
A good enough Software Transactional Memory (STM) system can be used
as an internal tool to do that.
This is what we are building into an “AME PyPy”.
The picture is darker for CPython, though there is a way too. The
problem is that when we say STM, we think about either GCC 4.7’s STM
support, or Hardware Transactional Memory (HTM). However, both
solutions are enough for a “GIL-less CPython”, but not
for “AME CPython”, due to capacity limitations. For the latter, we
need somehow to add some large-scale STM into the compiler.

Let me explain these points in more details.

The first point is in favor of the so-called Automatic Mutual Exclusion
approach. The issue with using threads (in any language with or without
a GIL) is that threads are fundamentally non-deterministic. In other
words, the programs’ behaviors are not reproductible at all, and worse,
we cannot even reason about it — it becomes quickly messy. We would
have to consider all possible combinations of code paths and timings,
and we cannot hope to write tests that cover all combinations. This
fact is often documented as one of the main blockers towards writing
successful multithreaded applications.

We need to solve this issue with a higher-level solution. Such
solutions exist theoretically, and Automatic Mutual Exclusion (AME) is
one of them. The idea of AME is that we divide the execution of each
thread into a number of “atomic blocks”. Each block is well-delimited
and typically large. Each block runs atomically, as if it acquired a
GIL for its whole duration. The trick is that internally we use
Transactional Memory, which is a technique that lets the system run the
atomic blocks from each thread in parallel, while giving the programmer
the illusion that the blocks have been run in some global serialized
order.

This doesn’t magically solve all possible issues, but it helps a lot: it
is far easier to reason in terms of a random ordering of large atomic
blocks than in terms of a random ordering of lines of code — not to
mention the mess that multithreaded C is, where even a random ordering
of instructions is not a sufficient model any more.

How do such atomic blocks look like? For example, a program might
contain a loop over all keys of a dictionary, performing some
“mostly-independent” work on each value. This is a typical example:
each atomic block is one iteration through the loop. By using the
technique described here, we can run the iterations in parallel
(e.g. using a thread pool) but using AME to ensure that they appear to
run serially.

In Python, we don’t care about the order in which the loop iterations
are done, because we are anyway iterating over the keys of a dictionary.
So we get exactly the same effect as before: the iterations still run in
some random order, but — and that’s the important point — they
appear to run in a
global serialized order. In other words, we introduced parallelism, but
only under the hood: from the programmer’s point of view, his program
still appears to run completely serially. Parallelisation as a
theoretically invisible optimization… more about the “theoretically”
in the next paragraph.

Note that randomness of order is not fundamental: they are techniques
building on top of AME that can be used to force the order of the
atomic blocks, if needed.

Talking more precisely about PyPy: the current prototype pypy-stm is
doing precisely this. In pypy-stm, the length of the atomic blocks is
selected in one of two ways: either explicitly or automatically.

The automatic selection gives blocks corresponding to some small number
of bytecodes, in which case we have merely a GIL-less Python: multiple
threads will appear to run serially, with the execution randomly
switching from one thread to another at bytecode boundaries, just like
in CPython.

The explicit selection is closer to what was described in the previous
section: someone — the programmer or the author of some library that
the programmer uses — will explicitly put with thread.atomic: in
the source, which delimitates an atomic block. For example, we can use
it to build a library that can be used to iterate over the keys of a
dictionary: instead of iterating over the dictionary directly, we would
use some custom utility which gives the elements “in parallel”. It
would give them by using internally a pool of threads, but enclosing
every handling of an element into such a with thread.atomic block.

This gives the nice illusion of a global serialized order, and thus
gives us a well-behaving model of the program’s behavior.

Restating this differently,
the only semantical difference between pypy-stm and
a regular PyPy or CPython is that it has thread.atomic, which is a
context manager that gives the illusion of forcing the GIL to not be
released during the execution of the corresponding block of code. Apart
from this addition, they are apparently identical.

Of course they are only semantically identical if we ignore performance:
pypy-stm uses multiple threads and can potentially benefit from that
on multicore machines. The drawback is: when does it benefit, and how
much? The answer to this question is not immediate. The programmer
will usually have to detect and locate places that cause too many
“conflicts” in the Transactional Memory sense. A conflict occurs when
two atomic blocks write to the same location, or when A reads it,
B writes it, but B finishes first and commits. A conflict
causes the execution of one atomic block to be aborted and restarted,
due to another block committing. Although the process is transparent,
if it occurs more than occasionally, then it has a negative impact on
performance.

There is no out-of-the-box perfect solution for solving all conflicts.
What we will need is more tools to detect them and deal with them, data
structures that are made aware of the risks of “internal” conflicts when
externally there shouldn’t be one, and so on. There is some work ahead.

The point here is that from the point of view of the final programmer,
we gets conflicts that we should resolve — but at any point, our
program is correct, even if it may not be yet as efficient as it could
be. This is the opposite of regular multithreading, where programs are
efficient but not as correct as they could be. In other words, as we
all know, we only have resources to do the easy 80% of the work and not
the remaining hard 20%. So in this model we get a program that has 80%
of the theoretical maximum of performance and it’s fine. In the regular
multithreading model we would instead only manage to remove 80% of the
bugs, and we are left with obscure rare crashes.

Couldn’t we do the same for CPython? The problem here is that
pypy-stm is implemented as a transformation step during translation,
which is not directly possible in CPython. Here are our options:

  • We could review and change the C code everywhere in CPython.
  • We use GCC 4.7, which supports some form of STM.
  • We wait until Intel’s next generation of CPUs comes out (“Haswell”)
    and use HTM.
  • We write our own C code transformation within a compiler (e.g. LLVM).

I will personally file the first solution in the “thanks but no thanks”
category. If anything, it will give us another fork of CPython that
will painfully struggle to keep not more than 3-4 versions behind, and
then eventually die. It is very unlikely to be ever merged into the
CPython trunk, because it would need changes everywhere. Not to
mention that these changes would be very experimental: tomorrow we might
figure out that different changes would have been better, and have to
start from scratch again.

Let us turn instead to the next two solutions. Both of these solutions
are geared toward small-scale transactions, but not long-running ones.
For example, I have no clue how to give GCC rules about performing I/O
in a transaction — this seems not supported at all; and moreover
looking at the STM library that is available so far to be linked with
the compiled program, it assumes short transactions only. By contrast,
when I say “long transaction” I mean transactions that can run for 0.1
seconds or more. To give you an idea, in 0.1 seconds a PyPy program
allocates and frees on the order of ~50MB of memory.

Intel’s Hardware Transactional Memory solution is both more flexible and
comes with a stricter limit. In one word, the transaction boundaries
are given by a pair of special CPU instructions that make the CPU enter
or leave “transactional” mode. If the transaction aborts, the CPU
cancels any change, rolls back to the “enter” instruction and causes
this instruction to return an error code instead of re-entering
transactional mode (a bit like a fork()). The software then detects
the error code. Typically, if transactions are rarely cancelled, it is
fine to fall back to a GIL-like solution just to redo these cancelled
transactions.

About the implementation: this is done by recording all the changes that
a transaction wants to do to the main memory, and keeping them invisible
to other CPUs. This is “easily” achieved by keeping them inside this
CPU’s local cache; rolling back is then just a matter of discarding a
part of this cache without committing it to memory. From this point of
view, there is a lot to bet that we are actually talking about the
regular per-core Level 1 and Level 2 caches — so any transaction that
cannot fully store its read and written data in the 64+256KB of the L1+L2
caches will abort.

So what does it mean? A Python interpreter overflows the L1 cache of
the CPU very quickly: just creating new Python function frames takes a
lot of memory (on the order of magnitude of 1/100 of the whole L1
cache). Adding a 256KB L2 cache into the picture helps, particularly
because it is highly associative and thus avoids a lot of fake conflicts.
However, as long as the HTM support is limited to L1+L2 caches,
it is not going to be enough to run an “AME Python” with any sort of
medium-to-long transaction. It can
run a “GIL-less Python”, though: just running a few hundred or even
thousand bytecodes at a time should fit in the L1+L2 caches, for most
bytecodes.

I would vaguely guess that it will take on the order of 10 years until
CPU cache sizes grow enough for a CPU in HTM mode to actually be able to
run 0.1-second transactions. (Of course in 10 years’ time a lot of other
things may occur too, including the whole Transactional Memory model
being displaced by something else.)

Let’s discuss now the last option: if neither GCC 4.7 nor HTM are
sufficient for an “AME CPython”, then we might want to
write our own C compiler patch (as either extra work on GCC 4.7, or an
extra pass to LLVM, for example).

We would have to deal with the fact that we get low-level information,
and somehow need to preserve interesting high-level bits through the
compiler up to the point at which our pass runs: for example, whether
the field we read is immutable or not. (This is important because some
common objects are immutable, e.g. PyIntObject. Immutable reads don’t
need to be recorded, whereas reads of mutable data must be protected
against other threads modifying them.) We can also have custom code to
handle the reference counters: e.g. not consider it a conflict if
multiple transactions have changed the same reference counter, but just
resolve it automatically at commit time. We are also free to handle I/O
in the way we want.

More generally, the advantage of this approach over both the current GCC
4.7 and over HTM is that we control the whole process. While this still
looks like a lot of work, it looks doable. It would be possible to come
up with a minimal patch of CPython that can be accepted into core
without too much troubles (e.g. to mark immutable fields and tweak the
refcounting macros), and keep all the cleverness inside the compiler
extension.

I would assume that a programming model specific to PyPy and not
applicable to CPython has little chances to catch on, as long as PyPy is
not the main Python interpreter (which looks unlikely to change anytime
soon). Thus as long as only PyPy has AME, it looks like it will not
become the main model of multicore usage in Python. However, I can
conclude with a more positive note than during the EuroPython
conference: it is a lot of work, but there is a more-or-less reasonable
way forward to have an AME version of CPython too.

In the meantime, pypy-stm is around the corner, and together with
tools developed on top of it, it might become really useful and used. I
hope that in the next few years this work will trigger enough motivation
for CPython to follow the ideas.


from Hacker News 50: http://morepypy.blogspot.de/2012/08/multicore-programming-in-pypy-and.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+hacker-news-feed-50+%28Hacker+News+50%29

Written by cwyalpha

八月 15, 2012 在 3:29 上午

发表在 Uncategorized

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