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PyPy - How to not write Virtual Machines forDynamic Languages
Armin Rigo
Institut für InformatikHeinrich-Heine-Universität Düsseldorf
ESUG 2007
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Scope
This talk is about:
implementing dynamic languages(with a focus on complicated ones)in a context of limited resources(academic, open source, or domain-specific)
Complicated = requiring a large VM
Smalltalk (etc...): typically small core VMPython (etc...): the VM contains quite a lot
Limited resourcesOnly near-complete implementations are really usefulMinimize implementer’s duplication of efforts
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Scope
This talk is about:
implementing dynamic languages(with a focus on complicated ones)in a context of limited resources(academic, open source, or domain-specific)
Complicated = requiring a large VM
Smalltalk (etc...): typically small core VMPython (etc...): the VM contains quite a lot
Limited resourcesOnly near-complete implementations are really usefulMinimize implementer’s duplication of efforts
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Scope
This talk is about:
implementing dynamic languages(with a focus on complicated ones)in a context of limited resources(academic, open source, or domain-specific)
Complicated = requiring a large VM
Smalltalk (etc...): typically small core VMPython (etc...): the VM contains quite a lot
Limited resourcesOnly near-complete implementations are really usefulMinimize implementer’s duplication of efforts
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Our point
Our point:
Do not write virtual machines “by hand”Instead, write interpreters in high-level languagesMeta-programming is your friend
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Common Approaches to VM construction
Using C directly (or C disguised as another language)
CPythonRubySpidermonkey (Mozilla’s JavaScript VM)but also: Squeak, Scheme48
Building on top of a general-purpose OO VMJython, IronPythonJRuby, IronRuby
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Implementing VMs in C
When writing a VM in C it is hard to reconcile:flexibility, maintainabilitysimplicity of the VMperformance (needs dynamic compilation techniques)
Python CaseCPython is a very simple bytecode VM, performance notgreatPsyco is a just-in-time-specializer, very complex, hard tomaintain, but good performanceStackless is a fork of CPython adding microthreads. It wasnever incorporated into CPython for complexity reasons
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Implementing VMs in C
When writing a VM in C it is hard to reconcile:flexibility, maintainabilitysimplicity of the VMperformance (needs dynamic compilation techniques)
Python CaseCPython is a very simple bytecode VM, performance notgreatPsyco is a just-in-time-specializer, very complex, hard tomaintain, but good performanceStackless is a fork of CPython adding microthreads. It wasnever incorporated into CPython for complexity reasons
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Compilers are a bad encoding of Semantics
to reach good performance levels, dynamic compilation isoften neededa dynamic compiler needs to encode language semanticsthis encoding is often obscure and hard to change
Python CasePsyco is a dynamic compiler for Pythonsynchronizing with CPython’s rapid development is a lot ofeffortmany of CPython’s new features not supported well
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Compilers are a bad encoding of Semantics
to reach good performance levels, dynamic compilation isoften neededa dynamic compiler needs to encode language semanticsthis encoding is often obscure and hard to change
Python CasePsyco is a dynamic compiler for Pythonsynchronizing with CPython’s rapid development is a lot ofeffortmany of CPython’s new features not supported well
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Fixing of Early Design Decisions
when starting a VM in C, many design decisions need tobe made upfrontexamples: memory management technique, threadingmodelthe decision is manifested throughout the VM sourcevery hard to change later
Python CaseCPython uses reference counting, increfs and decrefseverywhereCPython uses OS threads with one global lock, hard tochange to lightweight threads or finer locking
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Fixing of Early Design Decisions
when starting a VM in C, many design decisions need tobe made upfrontexamples: memory management technique, threadingmodelthe decision is manifested throughout the VM sourcevery hard to change later
Python CaseCPython uses reference counting, increfs and decrefseverywhereCPython uses OS threads with one global lock, hard tochange to lightweight threads or finer locking
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Implementation Proliferation
restrictions of the original implementation lead tore-implementations, forksall implementations need to be synchronized withlanguage evolutionlots of duplicate effort
Python Caseseveral serious implementations: CPython, Stackless,Psyco, Jython, IronPython, PyPythe implementations have various grades of compliance
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Implementation Proliferation
restrictions of the original implementation lead tore-implementations, forksall implementations need to be synchronized withlanguage evolutionlots of duplicate effort
Python Caseseveral serious implementations: CPython, Stackless,Psyco, Jython, IronPython, PyPythe implementations have various grades of compliance
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Implementing Languages on Top of General-PurposeOO VMs
users wish to have easy interoperation with thegeneral-purpose OO VMs used by the industry (JVM, CLR)therefore re-implementations of the language on the OOVMs are startedeven more implementation proliferationimplementing on top of an OO VM has its own set ofproblems
Python CaseJython is a Python-to-Java-bytecode compilerIronPython is a Python-to-CLR-bytecode compilerboth are slightly incompatible with the newest CPythonversion (especially Jython)
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Implementing Languages on Top of General-PurposeOO VMs
users wish to have easy interoperation with thegeneral-purpose OO VMs used by the industry (JVM, CLR)therefore re-implementations of the language on the OOVMs are startedeven more implementation proliferationimplementing on top of an OO VM has its own set ofproblems
Python CaseJython is a Python-to-Java-bytecode compilerIronPython is a Python-to-CLR-bytecode compilerboth are slightly incompatible with the newest CPythonversion (especially Jython)
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Benefits of implementing on top of OO VMs
higher level of implementationthe VM supplies a GC and mostly a JITbetter interoperability than what the C level providessome proponents believe that eventually one single VMshould be enough
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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The problems of OO VMs
some of the benefits of OO VMs don’t work out in practicemost immediate problem: it can be hard to map conceptsof the dynamic lang to the host OO VMperformance is often not improved, and can be very bad,because of the semantic mismatch between the dynamiclanguage and the host VMpoor interoperability with everything outside the OO VMin practice, one OO VM is not enough
Python CaseJython about 5 times slower than CPythonIronPython is about as fast as CPython (but someintrospection features missing)
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
The problems of OO VMs
some of the benefits of OO VMs don’t work out in practicemost immediate problem: it can be hard to map conceptsof the dynamic lang to the host OO VMperformance is often not improved, and can be very bad,because of the semantic mismatch between the dynamiclanguage and the host VMpoor interoperability with everything outside the OO VMin practice, one OO VM is not enough
Python CaseJython about 5 times slower than CPythonIronPython is about as fast as CPython (but someintrospection features missing)
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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PyPy’s Approach to VM Construction
Goal: achieve flexibility, simplicity and performance together
Approach: auto-generate VMs from high-level descriptionsof the language... using meta-programming techniques and aspectshigh-level description: an interpreter written in a high-levellanguage... which we translate (i.e. compile) to VMs running on topof various targets, like C/Posix, CLR, JVM
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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PyPy
PyPy = Python interpreter written in RPython + translationtoolchain for RPython
What is RPythonRPython is a subset of Pythonsubset chosen in such a way that type-inference can beperformedstill a high-level language (unlike SLang or Prescheme)...really a subset, can’t give a small example of code thatdoesn’t just look like Python :-)
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
PyPy
PyPy = Python interpreter written in RPython + translationtoolchain for RPython
What is RPythonRPython is a subset of Pythonsubset chosen in such a way that type-inference can beperformedstill a high-level language (unlike SLang or Prescheme)...really a subset, can’t give a small example of code thatdoesn’t just look like Python :-)
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Auto-generating VMs
high-level source: early design decisions not necessarywe need a custom translation toolchain to compile theinterpreter to a full VMmany aspects of the final VM are orthogonal to theinterpreter source: they are inserted during translationtranslation aspect ∼= monads, with more ad-hoc control
ExamplesGarbage Collection strategyThreading models (e.g. coroutines with CPS...)non-trivial translation aspect: auto-generating a dynamiccompiler from the interpreter
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Auto-generating VMs
high-level source: early design decisions not necessarywe need a custom translation toolchain to compile theinterpreter to a full VMmany aspects of the final VM are orthogonal to theinterpreter source: they are inserted during translationtranslation aspect ∼= monads, with more ad-hoc control
ExamplesGarbage Collection strategyThreading models (e.g. coroutines with CPS...)non-trivial translation aspect: auto-generating a dynamiccompiler from the interpreter
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Good Points of the Approach
Simplicity:
dynamic languages can be implemented in a high levellanguageseparation of concerns from low-level detailsa potential single-source-fits-all interpreter – lessduplication of effortsruns everywhere with the same semantics – no outdatedimplementations, no ties to any standard platform
PyPyarguably the most readable Python implementation so far
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Good Points of the Approach
Simplicity:
dynamic languages can be implemented in a high levellanguageseparation of concerns from low-level detailsa potential single-source-fits-all interpreter – lessduplication of effortsruns everywhere with the same semantics – no outdatedimplementations, no ties to any standard platform
PyPyarguably the most readable Python implementation so far
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Good Points of the Approach
Flexibility at all levels:
when writing the interpreter (high-level languages rule!)when adapting the translation toolchain as necessaryto break abstraction barriers when necessary
Exampleboxed integer objects, represented as tagged pointersmanual system-level RPython code
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Good Points of the Approach
Flexibility at all levels:
when writing the interpreter (high-level languages rule!)when adapting the translation toolchain as necessaryto break abstraction barriers when necessary
Exampleboxed integer objects, represented as tagged pointersmanual system-level RPython code
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Good Points of the Approach
Performance:
“reasonable” performancecan generate a dynamic compiler from the interpreter(work in progress, 60x faster on very simple Python code)
JIT compiler generator
almost orthogonal from the interpreter source - applicableto many languages, follows language evolution “for free”based on Partial Evaluationbenefits from a high-level interpreter and a tweakabletranslation toolchaingenerating a dynamic compiler is easier than generating astatic one!
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
pypy-logo
Good Points of the Approach
Performance:
“reasonable” performancecan generate a dynamic compiler from the interpreter(work in progress, 60x faster on very simple Python code)
JIT compiler generator
almost orthogonal from the interpreter source - applicableto many languages, follows language evolution “for free”based on Partial Evaluationbenefits from a high-level interpreter and a tweakabletranslation toolchaingenerating a dynamic compiler is easier than generating astatic one!
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Open Issues / Drawbacks / Further Work
writing the translation toolchain in the first place takes lotsof effort (but it can be reused)writing a good GC is still necessary. But: maybe we canreuse existing good GCs (e.g. from the Jikes RVM)?conceptually simple approach but many abstraction layersdynamic compiler generation seems to work, but needsmore efforts. Also: can we layer it on top of the JIT of ageneral purpose OO VM?
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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Conclusion / Meta-Points
high-level languages are suitable to implement dynamiclanguagesdoing so has many benefitsVMs shouldn’t be written by handPyPy’s concrete approach is not so importantdiversity is goodlet’s write more meta-programming toolchains!
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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For more information
PyPy
http://codespeak.net/pypy/
“Sprints”Main way we develop PyPyThey are programming camps, a few days to one weeklongWe may have one in Bern soon (PyPy+Squeak) and/or inGermany (JIT and other topics)
See alsoGoogle for the full paper corresponding to these slides that wassubmitted at Dyla’2007
Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
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