A hardware-driven profiling scheme for identifying program hot spots to support runtime optimization
ISCA '99 Proceedings of the 26th annual international symposium on Computer architecture
Partial method compilation using dynamic profile information
OOPSLA '01 Proceedings of the 16th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Java Virtual Machine Specification
Java Virtual Machine Specification
Dynamic Path Profile Aided Recompilation in a JAVA Just-In-Time Compiler
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
The simplest heuristics may be the best in Java JIT compilers
ACM SIGPLAN Notices
LaTTe: A Java VM Just-in-Time Compiler with Fast and Efficient Register Allocation
PACT '99 Proceedings of the 1999 International Conference on Parallel Architectures and Compilation Techniques
A brief history of just-in-time
ACM Computing Surveys (CSUR)
When and what to compile/optimize in a virtual machine?
ACM SIGPLAN Notices
JIST: Just-in-Time Scheduling Translation for Parallel Processors
ISPDC '04 Proceedings of the Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks
The java hotspotTM server compiler
JVM'01 Proceedings of the 2001 Symposium on JavaTM Virtual Machine Research and Technology Symposium - Volume 1
Design of JFluid: a profiling technology and tool based on dynamic bytecode instrumentation
Design of JFluid: a profiling technology and tool based on dynamic bytecode instrumentation
CC '09 Proceedings of the 18th International Conference on Compiler Construction: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009
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Modern Java Virtual Machines (JVM) commonly adopt Just-In-Time (JIT) compilation to speed up the execution of Java Bytecode. However, the effort of compiling a region of code is only worth if the code is frequently executed. Therefore, Selective Compilation is employed so that the JIT compiler is only invoked on those regions of code where most of the computation is performed (hot spots). The core task in Selective Compilation is to correctly identify the hot spots in a program. In our SeleKaffe prototype virtual machine, we introduce two heuristics aimed at detecting hot spots both statically, via bytecode analysis, and dynamically, via profiling information. Experimental results on a representative set of benchmarks show that our method selection strategy is more accurate than known strategies, and not significantly slower.