What is evolutionary computation?
IEEE Spectrum
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
On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
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
Fast searches for effective optimization phase sequences
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
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
Fast compiler optimisation evaluation using code-feature based performance prediction
Proceedings of the 4th international conference on Computing frontiers
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Proceedings of the International Symposium on Code Generation and Optimization
PetaBricks: a language and compiler for algorithmic choice
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
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
MiDataSets: creating the conditions for a more realistic evaluation of Iterative optimization
HiPEAC'07 Proceedings of the 2nd international conference on High performance embedded architectures and compilers
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)
An efficient evolutionary algorithm for solving incrementally structured problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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
Reducing training time in a one-shot machine learning-based compiler
LCPC'09 Proceedings of the 22nd international conference on Languages and Compilers for Parallel Computing
Parallel iterative compilation: using MapReduce to speedup machine learning in compilers
Proceedings of third international workshop on MapReduce and its Applications Date
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Optimizing programs to exploit the underlying hardware architecture is an important task. Much research has been done on enabling compilers to find the best set of code optimizations that can build the fastest and less resource-hungry executable for a given program. A common approach is iterative compilation, sometimes enriched by machine learning techniques. This provides good results, but requires extremely long compilation times and an initial training phase lasting even for days or weeks. We present long-term learning, a new algorithm that allows the compiler user to improve the performance of compiled programs with reduced compilation times with respect to iterative compilation, and without an initial training phase. Our algorithm does not just build good programs: it acquires knowledge every time a program is compiled and it uses such knowledge to learn compiler heuristics, without the need for an expert to manually define them. The heuristics are evolved during every compilation, by evaluating their effect on the generated programs. We present implementations of long-term learning on top of two different compilers, and experimental data gathered on multiple hardware configurations showing its effectiveness.