Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
A fast Fourier transform compiler
Proceedings of the ACM SIGPLAN 1999 conference on Programming language design and implementation
Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Optimizing Program Locality Through CMEs and GAs
Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques
Combining Models and Guided Empirical Search to Optimize for Multiple Levels of the Memory Hierarchy
Proceedings of the international symposium on Code generation and optimization
Model-guided autotuning of high-productivity languages for petascale computing
Proceedings of the 18th ACM international symposium on High performance distributed computing
An overview of the ECO project
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A script-based autotuning compiler system to generate high-performance CUDA code
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
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The goal of this work is a systematic approach to compiler optimization for simultaneously optimizing across multiple levels of the memory hierarchy. Our approach combines compiler models and heuristics with guided empirical search to take advantage of their complementary strengths. The models and heuristics limit the search to a small number of candidate implementations, and the empirical results provide accurate feedback information to the compiler. In previous work, we propose a compiler algorithm for deriving a set of parameterized solutions, followed by a model-guided empirical search to determine the best integer parameter values and select the best overall solution. This paper focuses on formalizing the process of deriving parameter values, which is a multi-variable optimization problem, and considers the role of AI search techniques in deriving a systematic framework for the search.