Optimally profiling and tracing programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
The nature of statistical learning theory
The nature of statistical learning theory
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
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
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Inducing heuristics to decide whether to schedule
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Adaptive java optimisation using instance-based learning
Proceedings of the 18th annual international conference on Supercomputing
Predicting Unroll Factors Using Supervised Classification
Proceedings of the international symposium on Code generation and optimization
Practical Path Profiling for Dynamic Optimizers
Proceedings of the international symposium on Code generation and optimization
Automatically constructing compiler optimization heuristics using supervised learning
Automatically constructing compiler optimization heuristics using supervised learning
Automatic Tuning of Inlining Heuristics
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
Method-specific dynamic compilation using logistic regression
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Fast compiler optimisation evaluation using code-feature based performance prediction
Proceedings of the 4th international conference on Computing frontiers
File access prediction with adjustable accuracy
PCC '02 Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International
Automating the construction of compiler heuristics using machine learning
Automating the construction of compiler heuristics using machine learning
Using PredictiveModeling for Cross-Program Design Space Exploration in Multicore Systems
PACT '07 Proceedings of the 16th International Conference on Parallel Architecture and Compilation Techniques
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Automatic Feature Generation for Machine Learning Based Optimizing Compilation
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Novel online profiling for virtual machines
Proceedings of the 6th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Hybrid optimizations: which optimization algorithm to use?
CC'06 Proceedings of the 15th international conference on Compiler Construction
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Optimising compilers rely on profiling to identify the target regions for optimising the input programme. Although profiling is accurate, it incurs a lot of overhead, an obstacle to achieving considerable performance improvement. Alternatively, machine learning-based offline prediction of hot methods that form vital target segments, is bound to eliminate the runtime overhead. In this work, we develop and implement support vector machines-based hot method prediction models trained on effective static programme features generated by a new 'knock-out' algorithm. When trained using low level virtual machine (LLVM) environment, it is possible to predict the frequently called and the long running hot methods with 61% and 68% accuracy. Selective optimisation of the predicted hot methods before programme execution provides substantial performance improvement over default LLVM optimisation.