Unexpected side effects of inline substitution: a case study
ACM Letters on Programming Languages and Systems (LOPLAS)
Towards better inlining decisions using inlining trials
LFP '94 Proceedings of the 1994 ACM conference on LISP and functional programming
Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Function inlining under code size constraints for embedded processors
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
A comparative study of static and profile-based heuristics for inlining
DYNAMO '00 Proceedings of the ACM SIGPLAN workshop on Dynamic and adaptive compilation and optimization
Adaptive optimization in the Jalapeño JVM
OOPSLA '00 Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
A Machine Learning Approach to Automatic Production of Compiler Heuristics
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Adaptive online context-sensitive inlining
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Inducing heuristics to decide whether to schedule
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
IBM Systems Journal
Cole: compiler optimization level exploration
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Blind Optimization for Exploiting Hardware Features
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
Automated just-in-time compiler tuning
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
An adaptive strategy for inline substitution
CC'08/ETAPS'08 Proceedings of the Joint European Conferences on Theory and Practice of Software 17th international conference on Compiler construction
Effective feature set construction for SVM-based hot method prediction and optimisation
International Journal of Computational Science and Engineering
A transactional memory with automatic performance tuning
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
Proceedings of the compilation of the co-located workshops on DSM'11, TMC'11, AGERE!'11, AOOPES'11, NEAT'11, & VMIL'11
Adaptive Source-Level Data Assignment to Dual Memory Banks
ACM Transactions on Embedded Computing Systems (TECS)
Selective search of inlining vectors for program optimization
Proceedings of the 9th conference on Computing Frontiers
On the determination of inlining vectors for program optimization
CC'13 Proceedings of the 22nd international conference on Compiler Construction
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Inlining improves the performance of programs by reducing the overhead of method invocation and increasing the opportunities for compiler optimization. Incorrect inlining decisions, however, can degrade both the running and compilation time of a program. This is especially important for a dynamically compiled language such as Java. Therefore, the heuristics that control inlining must be carefully tuned to achieve a good balance between these two costs to reduce overall total execution time. This paper develops a genetic algorithms based approach to automatically tune a dynamic compiler's internal inlining heuristic. We evaluate our technique within the Jikes RVM [1] compiler and show a 17% average reduction in total execution time on the SPECjvm98 benchmark suite on a Pentium-4. When applied to the DaCapo benchmark suite, our approach reduces total execution time by 37%% outperforming all existing techniques.