|Funding body:||EPSRC||Funding amount:||£618,883 Expenses|
|Start date:||2013-06-03||End date:||2017-08-02|
|Principle investigator(s):||Laurence Tratt||Co-investigator(s):||-|
|Collaborator(s):||Simon Thompson, Oracle Labs||Research staff:||Edward Barrett|
|Other details:||Grants on the Web entry|
The chief existing technique for composing language runtimes is to translate all languages in the composition down to a base language, most commonly the bytecode for one of the "big" Virtual Machines (VMs) - Java's HotSpot or .NET's CLR. Though this works well in some cases, it has two major problems. First, a VM will intentionally target a specific family of languages, and may not provide the primitives needed by languages outside that family. HotSpot, for example, does not support tail recursion or continuations, excluding many advanced languages. Second, the primitives that a VM exposes may not allow efficient execution of programs. For example, dynamically typed languages running on HotSpot run slower than their seemingly much less sophisticated "home brew" VMs.
The Cooler project takes a new approach to the composition problem. It hypothesises that meta-tracing will allow the efficient composition of arbitrary language runtimes. Meta-tracing is a recently developed technique that creates efficient VMs with custom Just-in-Time (JIT) compilers. First, language designers write an interpreter for their chosen language. When that interpreter executes a user's program, hot paths in the code are recorded ("traced"), optimised, and converted into machine code; subsequent calls then use that fast machine code rather than the slow interpreter. Meta-tracing is distinct from partial evaluation: it records actual actions executed by the interpreter on a specific user program. Meta-tracing is an exciting new technique for three reasons. First, it leads to fast VMs: the PyPy VM (a fully compatible reimplementation of Python) is over 5 times faster than CPython (the C-based Python VM) and Jython (Python on the JVM). Second, it requires few resources: a meta-tracing implementation of the Converge language was completed in less than 3 person months, and runs faster than CPython and Jython. Third, because the user writes the interpreter themselves, there is no bias to any particular family of languages.
The Cooler project will initially design the first language specifically designed for meta-tracing (rather than, as existing systems, reusing an unsuitable existing language). This will enable the exploration of various aspects of language runtime composition. First, cross-runtime sharing: how can different paradigms (e.g. imperative and functional) exchange data and behaviour? Second, optimisation: how can programs written in multiple paradigms be optimised (space and time)? Finally, the limits of the approach will be explored through known hard problems: cross-runtime garbage collection; concurrency; and to what extent runtimes not designed for composition can be composed. Ultimately, the project will allow users to compose together runtimes and programs in ways that are currently unfeasible.