Optimizing dynamically-dispatched calls with run-time type feedback
PLDI '94 Proceedings of the ACM SIGPLAN 1994 conference on Programming language design and implementation
Optimizing Dynamically-Typed Object-Oriented Languages With Polymorphic Inline Caches
ECOOP '91 Proceedings of the European Conference on Object-Oriented Programming
Representation-based just-in-time specialization and the psyco prototype for python
Proceedings of the 2004 ACM SIGPLAN symposium on Partial evaluation and semantics-based program manipulation
HotpathVM: an effective JIT compiler for resource-constrained devices
Proceedings of the 2nd international conference on Virtual execution environments
PyPy's approach to virtual machine construction
Companion to the 21st ACM SIGPLAN symposium on Object-oriented programming systems, languages, and applications
RPython: a step towards reconciling dynamically and statically typed OO languages
Proceedings of the 2007 symposium on Dynamic languages
Tracing the meta-level: PyPy's tracing JIT compiler
Proceedings of the 4th workshop on the Implementation, Compilation, Optimization of Object-Oriented Languages and Programming Systems
Tracing the meta-level: PyPy's tracing JIT compiler
Proceedings of the 4th workshop on the Implementation, Compilation, Optimization of Object-Oriented Languages and Programming Systems
SPUR: a trace-based JIT compiler for CIL
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
RATA: rapid atomic type analysis by abstract interpretation – application to javascript optimization
CC'10/ETAPS'10 Proceedings of the 19th joint European conference on Theory and Practice of Software, international conference on Compiler Construction
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The Common Language Infrastructure (CLI) is a virtual machine expressly designed for implementing statically typed languages such as C#, therefore programs written in dynamically typed languages are typically much slower than C# when executed on .NET. Recent developments show that Just In Time (JIT) compilers can exploit runtime type information to generate quite efficient code. Unfortunately, writing a JIT compiler is far from being simple. In this paper we report our positive experience with automatic generation of JIT compilers as supported by the PyPy infrastructure, by focusing on JIT compilation for .NET. Following this approach, we have in fact added a second layer of JIT compilation, by allowing dynamic generation of more efficient .NET bytecode, which in turn can be compiled to machine code by the .NET JIT compiler. The main and novel contribution of this paper is to show that this two-layers JIT technique is effective, since programs written in dynamic languages can run on .NET as fast as (and in some cases even faster than) the equivalent C# programs. The practicality of the approach is demonstrated by showing some promising experiments done with benchmarks written in a simple dynamic language.