Porting the PyPy JIT to JVM and MLVM
PyPy and its JIT generator
PyPy_ is an open source research project that aims to produce a
flexible and fast implementation of the Python language.
PyPy is divided into two main parts: the Python interpreter, which
implements the Python language and is written in RPython_, and the
Translation Toolchain (TT), written in Python, which transforms and
translates programs written in RPython into the final executables.
RPython is a subset of Python specifically designed to allow the TT to
analyze RPython programs and translate them into lower level, very
Currently, the TT of PyPy provides three complete backends that
generate C code, bytecode for CLI/.NET or bytecode for the JVM. By
using these backends, we can get Python implementations that run on a
standard C/Posix environment, on the CLI or on the JVM.
It is important to underline that the job of the TT is not limited to
translation into an efficient executable, but it actively transforms
the source interpreter by adding new features and translation aspects,
such as garbage collection, microthreading (like `Stackless Python`_),
The most exciting feature of the TT is the ability to apply partial
evaluation techniques to automatically turn the interpreter into a JIT
compiler which generates efficient code dynamically. The key idea
behind the PyPy JIT is to systematically delay compilation until we know all the
information useful for emitting optimized code, thus being potentially
much more efficient than all the current other alternatives (see the
"Related Work" section). This is done using a mechanism which can be seen as a
generalized version of polymorphic inline caches.
Currently, the PyPy JIT works only in conjunction with the C backend.
Early results are very good. The resulting Python interpreter
can run numerically intensive computations at roughly the same speed of C,
as shown by the `technical report`_ on the JIT.
Moreover, there is an experimental JIT backend that emits code for the
CLI; it is still a work in progress and very incomplete, but it shows
that it is possible to adapt the PyPy JIT to emit code for object
oriented virtual machines.
Porting the JIT to the JVM
The goal of this proposal is to extend the PyPy JIT to work in
conjunction with the JVM backend. After the work has been completed,
it will be possible to translate the interpreter into a Python
implementation that runs on top of the JVM and contains a JIT; the JIT
will dynamically translate part of Python programs into JVM bytecode,
which will then be executed by the underlying virtual machine.
Porting the JIT to the MLVM
As stated above, PyPy JIT for JVM would work by dynamically emitting
and loading JVM bytecode at runtime. Even if this approach has been
tried in a couple of projects (see the "Related Work" section), it has
to been said that the JVM was not originally designed for such
applications; for example, the process of loading a single method is
very expensive, since it involves the creation and loading of a
The new Da Vinci Machine contains a lot of interesting features that
could be effectively exploited by the PyPy JIT to produce an even more
efficient implementation of the Python language, as `John Rose said`_
after the talk with PyPy people.
Features of the MLVM that could be exploited by PyPy JIT include but
are not limited to: dynamic invocation, lightweight bytecode loading,
tail calls, etc.
Implementation-wise, the JIT backends for the plain JVM and for the
MLVM could share most of the code, with the latter making use of the
special features when needed.
Moreover, the experience of this project will help the MLVM team to
understand which features are really useful to implement dynamic
languages on top of the JVM and which one we still lack.
Due to the its strict dependency on PyPy, it will not possible to
release the result of the work as a separate and independent project.
In particular, to reach the goals of the proposal it will be necessary
to extensively modify parts of PyPy that are already there, as well as
write completely new code.
If the project goes to completion, the code developed will be
integrated into the PyPy codebase; if Sun requires us to release the code
under the SCA (thus sharing the copyright between the original author
and Sun itself), we will send to Sun a document in unified diff format
that extensively shows all and sole lines of code on which Sun will
have the copyright.
PyPy is already licensed under the extremely permissive MIT license,
so there are no legal copyright barriers preventing us from sharing
code in such a way.
The PyPy JIT is still under heavy development; potentially, the resulting
JIT compiler will be able to optimize a large number of Python
programs, but at the moment it gives the best results only with
computational intensive functions that use only operations between
We expect to get a pypy-jvm executable that can execute a function
with those characteristics at roughly the same speed as its equivalent
written in Java, excluding the costs of the JIT compilation itself,
which have not been optimized yet.
For an example of a function with is highly optimized by the PyPy JIT,
look at the `function f1`_: when executed by a pypy-c compiled with
JIT support, it runs roughly at the same speed as its C equivalent
compiled with `gcc -O0`.
Supporting and thus speeding up more parts of the Python language
is a separate task and since it requires changes to the
interpreter, it is out of the scope of this proposal. It is important
to underline that this work is independent from the backend being used,
so once the PyPy interpreter is fully optimized for the
JIT, PyPy for the JVM will automatically take advantage of these improvements
without needing to change the JIT backend for the JVM.
We also expect to find benchmarks in which the JIT that targets the
MLVM will perform better than the JIT that targets the plain JVM,
though it is hard to specify a precise commitment here without knowing
which features of the MLVM will be possible to use.
Relevance to the community
To have a working JIT for the JVM is an important step towards making PyPy
the fastest Python for the JVM. Moreover, due to the innovative
ideas implemented by PyPy, it is likely that Python could become
the fastest dynamic language of its class that runs on the top of the JVM.
Finally, PyPy is not limited to Python: it is entirely possible to
write interpreters for languages other than Python and translate them
with the TT; as a proof of concept, PyPy already contains
various degrees of completeness.
Since the JIT generator is independent of the Python languages, it
will be possible to automatically add a JIT compiler to every language
written using the PyPy TT; thus, PyPy could become a very attractive
environment to develop dynamic languages for the JVM.
Dependencies on Sun
There are no dependencies on Sun regarding the implementation of a JIT
compiler that targets the plain JVM. However, in order to implement a
JIT compiler that targets the new MLVM, we need the new features we
want to exploit to be implemented.
Dynamic generation of bytecode for object oriented virtual machine is
a hot topic:
- `this paper`_ shows how this technique is exploited to write an
efficient implementation of EcmaScript which runs on top of the JVM;
- Jython compiles Python source code to JVM bytecode; however,
unlike most compilers, the compilation phase occurs when the JVM
has already been started, by generating and loading bytecode on
the fly; despite emitting code at runtime, this kind of compiler
really works ahead of time (AOT), because the code is fully
emitted before the (Python) program starts, and it doesn't exploit
additional informations that would be available only at runtime
(most importantly informations about the types that each variable can
- JRuby supports interpretation, AOT compilation and JIT
compilation; when the JIT compilation is enabled, JRuby interprets
methods until a call threshold is reached, then it compiles the
method body to JVM bytecode to be executed from that point on;
however, even if the compilation is truly just in time, JRuby
doesn't exploit type information that is known only at runtime to
produce specialized, efficient versions of the function;
- in the .NET world, IronPython works more or less as Jython;
additionally, it exploits dynamic code generation to implement
`Polymorphic Inline Caches`_.
The PyPy JIT is different from all of these, because runtime and compile
time are continuously intermixed; by waiting until the very last
possible moment to emit code, the JIT compiler is able to exploit all
the runtime information, including that which is only available late
in the process, e.g. the
exact type of all the variables involved; thus, it can generate many
specialized, fast versions of each function, which in theory could run
at the same speed of manually written Java code.
Moreover, the JIT compiler is automatically generated by the TT: we
believe, based on previous experiences with Psyco_, that manually
writing a JIT compiler of that kind is hard and error prone,
especially when the source language is as complex as Python; by
writing a JIT compiler generator, we get JIT compilers that are
correct by design for all languages implemented through the TT for
Antonio Cuni (email@example.com) is one of the core developers of
PyPy; he is the main author of the CLI backend, and the coauthor of
the JVM backend; recently, he began working on an experimental CLI
backend for the JIT. Currently, he is a PhD student at Univerist�
degli Studi di Genova, doing research in the area of implementation of
dynamic languages on top of object oriented virtual machines.
Carl Friedrich Bolz (firstname.lastname@example.org) also is a core developer of
PyPy. He worked on various areas including the garbage collectors,
optimizations and the Python interpreter. He is currently doing a
Master's thesis at the Heinrich-Heine-Universit�t D�sseldorf about
improving PyPy's JIT compiler generator.
Nicholas Matsakis (email@example.com) is also a core developer of
PyPy. He was the primary developer of the current JVM backend for PyPy, and
has participated in several PyPy sprints on various topics. He is currently
doing his PhD at ETH Zurich under the tutelage of Professor Thomas Gross.
.. _PyPy: http://codespeak.net/pypy
.. _RPython: http://codespeak.net/pypy/dist/pypy/doc/coding-guide.html#rpython
.. _`Stackless Python`: http://www.stackless.com/
.. _`technical report`: http://codespeak.net/pypy/extradoc/eu-report/D08.2_JIT_Compiler_Architecture-2007-05-01.pdf
.. _`John Rose said`: http://blogs.sun.com/jrose/entry/a_day_with_pypy
.. _Jython: http://www.jython.org
.. _`function f1`: http://codespeak.net/svn/pypy/dist/demo/jit/f1.py
.. _`this paper`: http://www.ics.uci.edu/~franz/Site/pubs-pdf/ICS-TR-07-10.pdf
.. _`Polymorphic Inline Caches`: http://www.cs.ucsb.edu/~urs/oocsb/papers/ecoop91.pdf
.. _Psyco: http://psyco.sourceforge.net/