Neural networks for pattern recognition
Neural networks for pattern recognition
Evidence-based static branch prediction using machine learning
ACM Transactions on Programming Languages and Systems (TOPLAS)
Learning to schedule straight-line code
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Compiler optimization-space exploration
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
A comparison of empirical and model-driven optimization
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
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
Finding effective compilation sequences
Proceedings of the 2004 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
IBM Systems Journal
Method-level phase behavior in java workloads
OOPSLA '04 Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Statistical Selection of Compiler Options
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Predicting Unroll Factors Using Supervised Classification
Proceedings of the international symposium on Code generation and optimization
A Model-Based Framework: An Approach for Profit-Driven Optimization
Proceedings of the international symposium on Code generation and optimization
ACME: adaptive compilation made efficient
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Probabilistic source-level optimisation of embedded programs
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Exhaustive Optimization Phase Order Space Exploration
Proceedings of the International Symposium on Code Generation and Optimization
Online performance auditing: using hot optimizations without getting burned
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Fast, automatic, procedure-level performance tuning
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
The DaCapo benchmarks: java benchmarking development and analysis
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Proceedings of the International Symposium on Code Generation and Optimization
PEAK—a fast and effective performance tuning system via compiler optimization orchestration
ACM Transactions on Programming Languages and Systems (TOPLAS)
Cole: compiler optimization level exploration
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
Practical exhaustive optimization phase order exploration and evaluation
ACM Transactions on Architecture and Code Optimization (TACO)
Automatic Feature Generation for Machine Learning Based Optimizing Compilation
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Improving both the performance benefits and speed of optimization phase sequence searches
Proceedings of the ACM SIGPLAN/SIGBED 2010 conference on Languages, compilers, and tools for embedded systems
Automated just-in-time compiler tuning
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Automatic creation of tile size selection models
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
A cost-aware parallel workload allocation approach based on machine learning techniques
NPC'07 Proceedings of the 2007 IFIP international conference on Network and parallel computing
Applied inference: Case studies in microarchitectural design
ACM Transactions on Architecture and Code Optimization (TACO)
Probabilistic auto-tuning for architectures with complex constraints
Proceedings of the 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era
Effective feature set construction for SVM-based hot method prediction and optimisation
International Journal of Computational Science and Engineering
An evaluation of different modeling techniques for iterative compilation
CASES '11 Proceedings of the 14th international conference on Compilers, architectures and synthesis for embedded systems
A step towards transparent integration of input-consciousness into dynamic program optimizations
Proceedings of the 2011 ACM international conference on Object oriented programming systems languages and applications
A transactional memory with automatic performance tuning
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
Automatic static feature generation for compiler optimization problems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Using machines to learn method-specific compilation strategies
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Mitigating the compiler optimization phase-ordering problem using machine learning
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Exploiting inter-sequence correlations for program behavior prediction
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Finding good optimization sequences covering program space
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
Performance potential of optimization phase selection during dynamic JIT compilation
Proceedings of the 9th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Automatic feature generation for machine learning--based optimising compilation
ACM Transactions on Architecture and Code Optimization (TACO)
Exploring single and multilevel JIT compilation policy for modern machines 1
ACM Transactions on Architecture and Code Optimization (TACO)
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
| Hi-index | 0.00 |
Determining the best set of optimizations to apply to a program has been a long standing problem for compiler writers. To reduce the complexity of this task, existing approaches typically apply the same set of optimizations to all procedures within a program, without regard to their particular structure. This paper develops a new method-specific approach that automatically selects the best optimizations on a per method basis within a dynamic compiler. Our approach uses the machine learning technique of logistic regression to automatically derive a predictive model that determines which optimizations to apply based on the features of a method. This technique is implemented in the Jikes RVM Java JIT compiler. Using this approach we reduce the average total execution time of the SPECjvm98 benchmarks by 29%. When the same heuristic is applied to the DaCapo+ benchmark suite, we obtain an average 33% reduction over the default level O2 setting.