Practicing JUDO: Java under dynamic optimizations
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Adaptive optimization in the Jalapeño JVM
OOPSLA '00 Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Online feedback-directed optimization of Java
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
FALCON: A MATLAB Interactive Restructuring Compiler
LCPC '95 Proceedings of the 8th International Workshop on Languages and Compilers for Parallel Computing
An Empirical Study of Method In-lining for a Java Just-in-Time Compiler
Proceedings of the 2nd Java Virtual Machine Research and Technology Symposium
Design, implementation and evaluation of adaptive recompilation with on-stack replacement
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Adaptive online context-sensitive inlining
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
The java hotspotTM server compiler
JVM'01 Proceedings of the 2001 Symposium on JavaTM Virtual Machine Research and Technology Symposium - Volume 1
Prospect: a compiler framework for speculative parallelization
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Optimizing MATLAB through just-in-time specialization
CC'10/ETAPS'10 Proceedings of the 19th joint European conference on Theory and Practice of Software, international conference on Compiler Construction
Optimizing MATLAB feval with dynamic techniques
Proceedings of the 9th symposium on Dynamic languages
Hi-index | 0.00 |
On-stack replacement (OSR) is a technique that allows a virtual machine to interrupt running code during the execution of a function/method, to re-optimize the function on-the-fly using an optimizing JIT compiler, and then to resume the interrupted function at the point and state at which it was interrupted. OSR is particularly useful for programs with potentially long-running loops, as it allows dynamic optimization of those loops as soon as they become hot. This paper presents a modular approach to implementing OSR for the LLVM compiler infrastructure. This is an important step forward because LLVM is gaining popular support, and adding the OSR capability allows compiler developers to develop new dynamic techniques. In particular, it will enable more sophisticated LLVM-based JIT compiler approaches. Indeed, other compiler/VM developers can use our approach because it is a clean modular addition to the standard LLVM distribution. Further, our approach is defined completely at the LLVM-IR level and thus does not require any modifications to the target code generation. The OSR implementation can be used by different compilers to support a variety of dynamic optimizations. As a demonstration of our OSR approach, we have used it to support dynamic inlining in McVM. McVM is a virtual machine for MATLAB which uses a LLVM-based JIT compiler. MATLAB is a popular dynamic language for scientific and engineering applications that typically manipulate large matrices and often contain long-running loops, and is thus an ideal target for dynamic JIT compilation and OSRs. Using our McVM example, we demonstrate reasonable overheads for our benchmark set, and performance improvements when using it to perform dynamic inlining.