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PyPy - How to not write Virtual Machines for Dynamic Languages. ESUG 2007, Lugano

PyPy - How to not write Virtual Machines for Dynamic Languages. ESUG 2007, Lugano

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  • 1. pypy-logo PyPy - How to not write Virtual Machines for Dynamic Languages Armin Rigo Institut für Informatik Heinrich-Heine-Universität Düsseldorf ESUG 2007 Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 2. 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) Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 3. 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 VM Python (etc...): the VM contains quite a lot Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 4. 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 VM Python (etc...): the VM contains quite a lot Limited resources Only near-complete implementations are really useful Minimize implementer’s duplication of efforts Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 5. pypy-logo Our point Our point: Do not write virtual machines “by hand” Instead, write interpreters in high-level languages Meta-programming is your friend Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 6. pypy-logo Common Approaches to VM construction Using C directly (or C disguised as another language) CPython Ruby Spidermonkey (Mozilla’s JavaScript VM) but also: Squeak, Scheme48 Building on top of a general-purpose OO VM Jython, IronPython JRuby, IronRuby Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 7. pypy-logo Implementing VMs in C When writing a VM in C it is hard to reconcile: flexibility, maintainability simplicity of the VM performance (needs dynamic compilation techniques) Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 8. pypy-logo Implementing VMs in C When writing a VM in C it is hard to reconcile: flexibility, maintainability simplicity of the VM performance (needs dynamic compilation techniques) Python Case CPython is a very simple bytecode VM, performance not great Psyco is a just-in-time-specializer, very complex, hard to maintain, but good performance Stackless is a fork of CPython adding microthreads. It was never incorporated into CPython for complexity reasons Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 9. pypy-logo Compilers are a bad encoding of Semantics to reach good performance levels, dynamic compilation is often needed a dynamic compiler needs to encode language semantics this encoding is often obscure and hard to change Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 10. pypy-logo Compilers are a bad encoding of Semantics to reach good performance levels, dynamic compilation is often needed a dynamic compiler needs to encode language semantics this encoding is often obscure and hard to change Python Case Psyco is a dynamic compiler for Python synchronizing with CPython’s rapid development is a lot of effort many of CPython’s new features not supported well Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 11. pypy-logo Fixing of Early Design Decisions when starting a VM in C, many design decisions need to be made upfront examples: memory management technique, threading model the decision is manifested throughout the VM source very hard to change later Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 12. pypy-logo Fixing of Early Design Decisions when starting a VM in C, many design decisions need to be made upfront examples: memory management technique, threading model the decision is manifested throughout the VM source very hard to change later Python Case CPython uses reference counting, increfs and decrefs everywhere CPython uses OS threads with one global lock, hard to change to lightweight threads or finer locking Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 13. pypy-logo Implementation Proliferation restrictions of the original implementation lead to re-implementations, forks all implementations need to be synchronized with language evolution lots of duplicate effort Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 14. pypy-logo Implementation Proliferation restrictions of the original implementation lead to re-implementations, forks all implementations need to be synchronized with language evolution lots of duplicate effort Python Case several serious implementations: CPython, Stackless, Psyco, Jython, IronPython, PyPy the implementations have various grades of compliance Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 15. pypy-logo Implementing Languages on Top of General-Purpose OO VMs users wish to have easy interoperation with the general-purpose OO VMs used by the industry (JVM, CLR) therefore re-implementations of the language on the OO VMs are started even more implementation proliferation implementing on top of an OO VM has its own set of problems Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 16. pypy-logo Implementing Languages on Top of General-Purpose OO VMs users wish to have easy interoperation with the general-purpose OO VMs used by the industry (JVM, CLR) therefore re-implementations of the language on the OO VMs are started even more implementation proliferation implementing on top of an OO VM has its own set of problems Python Case Jython is a Python-to-Java-bytecode compiler IronPython is a Python-to-CLR-bytecode compiler both are slightly incompatible with the newest CPython version (especially Jython) Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 17. pypy-logo Benefits of implementing on top of OO VMs higher level of implementation the VM supplies a GC and mostly a JIT better interoperability than what the C level provides some proponents believe that eventually one single VM should be enough Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 18. pypy-logo The problems of OO VMs some of the benefits of OO VMs don’t work out in practice most immediate problem: it can be hard to map concepts of the dynamic lang to the host OO VM performance is often not improved, and can be very bad, because of the semantic mismatch between the dynamic language and the host VM poor interoperability with everything outside the OO VM in practice, one OO VM is not enough Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 19. pypy-logo The problems of OO VMs some of the benefits of OO VMs don’t work out in practice most immediate problem: it can be hard to map concepts of the dynamic lang to the host OO VM performance is often not improved, and can be very bad, because of the semantic mismatch between the dynamic language and the host VM poor interoperability with everything outside the OO VM in practice, one OO VM is not enough Python Case Jython about 5 times slower than CPython IronPython is about as fast as CPython (but some introspection features missing) Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 20. pypy-logo PyPy’s Approach to VM Construction Goal: achieve flexibility, simplicity and performance together Approach: auto-generate VMs from high-level descriptions of the language ... using meta-programming techniques and aspects high-level description: an interpreter written in a high-level language ... which we translate (i.e. compile) to VMs running on top of various targets, like C/Posix, CLR, JVM Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 21. pypy-logo PyPy PyPy = Python interpreter written in RPython + translation toolchain for RPython Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 22. pypy-logo PyPy PyPy = Python interpreter written in RPython + translation toolchain for RPython What is RPython RPython is a subset of Python subset chosen in such a way that type-inference can be performed still a high-level language (unlike SLang or Prescheme) ...really a subset, can’t give a small example of code that doesn’t just look like Python :-) Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 23. pypy-logo Auto-generating VMs high-level source: early design decisions not necessary we need a custom translation toolchain to compile the interpreter to a full VM many aspects of the final VM are orthogonal to the interpreter source: they are inserted during translation translation aspect ∼= monads, with more ad-hoc control Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 24. pypy-logo Auto-generating VMs high-level source: early design decisions not necessary we need a custom translation toolchain to compile the interpreter to a full VM many aspects of the final VM are orthogonal to the interpreter source: they are inserted during translation translation aspect ∼= monads, with more ad-hoc control Examples Garbage Collection strategy Threading models (e.g. coroutines with CPS...) non-trivial translation aspect: auto-generating a dynamic compiler from the interpreter Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 25. pypy-logo Good Points of the Approach Simplicity: dynamic languages can be implemented in a high level language separation of concerns from low-level details a potential single-source-fits-all interpreter – less duplication of efforts runs everywhere with the same semantics – no outdated implementations, no ties to any standard platform Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 26. pypy-logo Good Points of the Approach Simplicity: dynamic languages can be implemented in a high level language separation of concerns from low-level details a potential single-source-fits-all interpreter – less duplication of efforts runs everywhere with the same semantics – no outdated implementations, no ties to any standard platform PyPy arguably the most readable Python implementation so far Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 27. 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 necessary to break abstraction barriers when necessary Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 28. 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 necessary to break abstraction barriers when necessary Example boxed integer objects, represented as tagged pointers manual system-level RPython code Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 29. pypy-logo Good Points of the Approach Performance: “reasonable” performance can generate a dynamic compiler from the interpreter (work in progress, 60x faster on very simple Python code) Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 30. pypy-logo Good Points of the Approach Performance: “reasonable” performance can 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 - applicable to many languages, follows language evolution “for free” based on Partial Evaluation benefits from a high-level interpreter and a tweakable translation toolchain generating a dynamic compiler is easier than generating a static one! Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 31. pypy-logo Open Issues / Drawbacks / Further Work writing the translation toolchain in the first place takes lots of effort (but it can be reused) writing a good GC is still necessary. But: maybe we can reuse existing good GCs (e.g. from the Jikes RVM)? conceptually simple approach but many abstraction layers dynamic compiler generation seems to work, but needs more efforts. Also: can we layer it on top of the JIT of a general purpose OO VM? Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 32. pypy-logo Conclusion / Meta-Points high-level languages are suitable to implement dynamic languages doing so has many benefits VMs shouldn’t be written by hand PyPy’s concrete approach is not so important diversity is good let’s write more meta-programming toolchains! Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages
  • 33. pypy-logo For more information PyPy http://codespeak.net/pypy/ “Sprints” Main way we develop PyPy They are programming camps, a few days to one week long We may have one in Bern soon (PyPy+Squeak) and/or in Germany (JIT and other topics) See also Google for the full paper corresponding to these slides that was submitted at Dyla’2007 Armin Rigo PyPy - How to not write Virtual Machines for Dynamic Languages

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