A user level program transformation tool
ICS '98 Proceedings of the 12th international conference on Supercomputing
Optimized unrolling of nested loops
Proceedings of the 14th international conference on Supercomputing
Computer aided hand tuning (CAHT): “applying case-based reasoning to performance tuning”
ICS '01 Proceedings of the 15th international conference on Supercomputing
Aggressive Loop Unrolling in a Retargetable Optimizing Compiler
CC '96 Proceedings of the 6th International Conference on Compiler Construction
A method for estimating optimal unrolling times for nested loops
ISPAN '97 Proceedings of the 1997 International Symposium on Parallel Architectures, Algorithms and Networks
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
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
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
Optimizing general purpose compiler optimization
Proceedings of the 2nd conference on Computing frontiers
Generating new general compiler optimization settings
Proceedings of the 19th annual international conference on Supercomputing
Automatic Selection of Compiler Options Using Non-parametric Inferential Statistics
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
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
Method-specific dynamic compilation using logistic regression
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Automatic performance model construction for the fast software exploration of new hardware designs
CASES '06 Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems
Fast compiler optimisation evaluation using code-feature based performance prediction
Proceedings of the 4th international conference on Computing frontiers
Iterative Optimization in the Polyhedral Model: Part I, One-Dimensional Time
Proceedings of the International Symposium on Code Generation and Optimization
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Proceedings of the International Symposium on Code Generation and Optimization
Instruction scheduling using evolutionary programming
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Towards Machine Learning of Grammars and Compilers of Programming Languages
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
HiPEAC '09 Proceedings of the 4th International Conference on High Performance Embedded Architectures and Compilers
Quick and Practical Run-Time Evaluation of Multiple Program Optimizations
Transactions on High-Performance Embedded Architectures and Compilers I
Raced profiles: efficient selection of competing compiler optimizations
Proceedings of the 2009 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
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 13th International Workshop on Software & Compilers for Embedded Systems
Practical aggregation of semantical program properties for machine learning based optimization
CASES '10 Proceedings of the 2010 international conference on Compilers, architectures and synthesis for embedded systems
Collective optimization: A practical collaborative approach
ACM Transactions on Architecture and Code Optimization (TACO)
On the impact of data input sets on statistical compiler tuning
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A workload-aware mapping approach for data-parallel programs
Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers
Brainy: effective selection of data structures
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
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
Using machine learning to improve automatic vectorization
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
A transactional memory with automatic performance tuning
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
A practical method for quickly evaluating program optimizations
HiPEAC'05 Proceedings of the First international conference on High Performance Embedded Architectures and Compilers
Hybrid optimizations: which optimization algorithm to use?
CC'06 Proceedings of the 15th international conference on Compiler Construction
Predictive modeling in a polyhedral optimization space
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Selective search of inlining vectors for program optimization
Proceedings of the 9th conference on Computing Frontiers
Using graph-based program characterization for predictive modeling
Proceedings of the Tenth 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
Continuous learning of compiler heuristics
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
On the determination of inlining vectors for program optimization
CC'13 Proceedings of the 22nd international conference on Compiler Construction
Hybrid type legalization for a sparse SIMD instruction set
ACM Transactions on Architecture and Code Optimization (TACO)
Automatic feature generation for machine learning--based optimising compilation
ACM Transactions on Architecture and Code Optimization (TACO)
Tools for machine-learning-based empirical autotuning and specialization
International Journal of High Performance Computing Applications
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Achieving high performance on modern processors heavily relies on the compiler optimizations to exploit the microprocessor architecture. The efficiency of optimization directly depends on the compiler heuristics. These heuristics must be target-specific and each new processor generation requires heuristics reengineering.In this paper, we address the automatic generation of optimization heuristics for a target processor by machine learning. We evaluate the potential of this method on an always legal and simple transformation: loop unrolling. Though simple to implement, this transformation may have strong effects on program execution (good or bad). However deciding to perform the transformation or not is difficult since many interacting parameters must be taken into account. So we propose a machine learning approach.We try to answer the following questions: is it possible to devise a learning process that captures the relevant parameters involved in loop unrolling performance? Does the Machine Learning Based Heuristics achieve better performance than existing ones?