Fwd: [Python-Dev] A new JIT compiler for a faster CPython?
- Dag Sverre Seljebotn
- 2012-07-17 @ 18:47
Hi, did the numba list get the response to this on python-dev that I
CC-ed to the numba list? Librelist asked me to confirm signup *again*,
so either there's a bug (and I don't know whether it got through) or
somebody unsubscribed me...
Anyway, I think some of you definitely want to get in touch with Victor
-------- Original Message --------
Subject: [Python-Dev] A new JIT compiler for a faster CPython?
Date: Tue, 17 Jul 2012 20:38:37 +0200
From: Victor Stinner <firstname.lastname@example.org>
To: Python Dev <Python-Dev@python.org>
I would like to write yet another JIT compiler for CPython. Before
writing anything, I would like your opinion because I don't know well
other Python compilers. I also want to prepare a possible integration
into CPython since the beginning of the project, or at least stay very
close to the CPython project (and CPython developers!). I did not
understand exactly why Unladen Swallow and psyco projects failed, so
please tell me if you think that my project is going to fail too!
== Why? ==
CPython is still the reference implementation, new features are first
added to this implementation (ex: PyPy is not supporting Python 3 yet,
but there is a project to support Python 3). Some projects still rely
on low level properties of CPython, especially its C API (ex: numpy;
PyPy has a cpyext module to emulate the CPython C API).
A JIT is the most promising solution to speed up the main evaluation
loop: using a JIT, it is possible to compile a function for a specific
type on the fly and so enable deeper optimizations.
psyco is no more maintained. It had its own JIT which is complex to
maintain. For example, it is hard to port it to a new hardware.
LLVM is fast and the next version will be faster. LLVM has a
community, a documentation, a lot of tools and is active.
There are many Python compilers which are very fast, but most of them
only support a subset of Python or require to modify the code (ex:
specify the type of all parameters and variables). For example, you
cannot run Django with Shredskin.
IMO PyPy is complex and hard to maintain. PyPy has a design completly
different than CPython and is much faster and has a better memory
footprint. I don't expect to be as fast as PyPy, just faster than
== General idea ==
I don't want to replace CPython. This is an important point. All
others Python compilers try to write something completly new, which is
an huge task and is a problem to stay compatible with CPython. I would
like to reuse as much as possible code of CPython and don't try to
fight against the GIL or reference counting, but try to cooperate
I would like to use a JIT to generate specialized functions for a
combinaison of arguments types. Specialization enables more
optimizations. I would like to use LLVM because LLVM is an active
project, has many developers and users, is fast and the next version
will be faster! LLVM already supports common optimizations like
My idea is to emit the same code than ceval.c from the bytecode to be
fully compatible with CPython, and then write a JIT to optimize
functions for a specific type.
== Roadmap ==
-- Milestone 1: Proof of concept --
* Use the bytecode produced by CPython parser and compiler
* Only compile a single function
* Emit the same code than ceval.c using LLVM, but without tracing,
exceptions nor signal handling (they will be added later)
* Support compiling and calling the following functions: "def func(a,
b): return a+b"
The pymothoa project can be used as a base to implement quickly such
proof of concept.
-- Milestone 2: Specialized function for the int type --
* Use type annotation to generate specialized functions for the int type
* Use C int with a guard detecting integer overflow to fallback on
-- Milestone 3: JIT --
* Depending on the type seen at runtime, recompile the function to
generate specialized functions
* Use guard to fallback to a generic implementation if the type is
not the expected type
* Drop maybe the code using function annotations
At this step, we can start to benchmark to check if the (JIT) compiler
is faster than CPython.
-- Later (unsorted ideas) --
* Support exceptions
* Full support of Python
- list comprehension
- avoid reference counting when possible
- avoid temporary objects when possible
- release the GIL when possible
- inlining: should be very interesting with list comprehension
- unroll loops?
- lazy creation of the frame?
* Use registers instead of a stack in the "evaluation loop"?
* Add code to allow tracing and profiling
* Add code to handle signals (pending calls)
* Write a compiler using the AST, with a fallback to the bytecode?
(would it be faster? easier or more complex to maintain?)
* Test LLVM optimizers
* Compile a whole module or even a whole program
* Reduce memory footprint
* Type annotation to help the optimizer? (with guards?)
* "const" annotation to help the optimizer? (with guards?)
* Support any build option of Python:
- support Python 2 (2.5, 2.6, 2.7) and 3 (3.1, 3.2, 3.3, 3.4)
- support narrow and wide mode: flag at runtime?
- support debug and release mode: flag at runtime?
- support 32 and 64 bits mode on Windows?
== Other Python VM and compilers ==
-- Fully Python compliant --
* `PyPy <http://pypy.org/>`_
* `Jython <http://www.jython.org/>`_ based on the JVM
* `IronPython <http://ironpython.net/>`_ based on the .NET VM
* `Unladen Swallow <http://code.google.com/p/unladen-swallow/>`_ fork
of CPython 2.6 using LLVM
- `Unladen Swallow Retrospective
- `PEP 3146 <http://python.org/dev/peps/pep-3146/>`_
* `psyco <http://psyco.sourceforge.net/>`_ (fully Python compliant?),
no more maintained
-- Subset of Python to C++ --
* `Nuitka <http://www.nuitka.net/pages/overview.html>`_
* `Python2C <http://strout.net/info/coding/python/ai/python2c.py>`_
* `Shedskin <http://code.google.com/p/shedskin/>`_
* `pythran <https://github.com/serge-sans-paille/pythran>`_ (no
class, set, dict, exception, file handling, ...)
-- Subset of Python --
* `pymothoa <http://code.google.com/p/pymothoa/>`_: use LLVM; don't
support classes nor exceptions.
* `unpython <http://code.google.com/p/unpython/>`_: Python to C
* `Perthon <http://perthon.sourceforge.net/>`_: Python to Perl
* `Copperhead <http://copperhead.github.com/>`_: Python to GPU (Nvidia)
-- Language very close to Python --
* `Cython <http://www.cython.org/>`_: "Cython is a programming
language based on Python, with extra syntax allowing for optional
static type declarations." Based on `Pyrex
== See also ==
* `Volunteer developed free-threaded cross platform virtual machines?
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