PLDI '92 Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation
Practical adaption of the global optimization algorithm of Morel and Renvoise
ACM Transactions on Programming Languages and Systems (TOPLAS)
A new algorithm for partial redundancy elimination based on SSA form
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
Advanced compiler design and implementation
Advanced compiler design and implementation
Global optimization by suppression of partial redundancies
Communications of the ACM
A portable machine-independent global optimizer--design and measurements
A portable machine-independent global optimizer--design and measurements
Escape analysis in the context of dynamic compilation and deoptimization
Proceedings of the 1st ACM/USENIX international conference on Virtual execution environments
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
Trace-based just-in-time type specialization for dynamic languages
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
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
Towards a jitting VM for prolog execution
Proceedings of the 12th international ACM SIGPLAN symposium on Principles and practice of declarative programming
SPUR: a trace-based JIT compiler for CIL
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Allocation removal by partial evaluation in a tracing JIT
Proceedings of the 20th ACM SIGPLAN workshop on Partial evaluation and program manipulation
Runtime feedback in a meta-tracing JIT for efficient dynamic languages
Proceedings of the 6th Workshop on Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems
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One of the nice properties of a tracing just-in-time compiler (JIT) is that many of its optimizations are simple, requiring one forward pass only. This is not true for loop-invariant code motion which is a very important optimization for code with tight kernels. Especially for dynamic languages that typically perform quite a lot of loop invariant type checking, boxed value unwrapping and virtual method lookups. In this paper we explain a scheme pioneered within the context of the LuaJIT project for making basic optimizations loop-aware by using a simple pre-processing step on the trace without changing the optimizations themselves. We have implemented the scheme in RPython's tracing JIT compiler. PyPy's Python JIT executing simple numerical kernels can become up to two times faster, bringing the performance into the ballpark of static language compilers.