Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Adaptive Optimizing Compilers for the 21st Century
The Journal of Supercomputing
Compiler optimization-space exploration
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation
PACT '00 Proceedings of the 2000 International Conference on Parallel Architectures and Compilation Techniques
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Predicting Unroll Factors Using Supervised Classification
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
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
Fast and Effective Orchestration of Compiler Optimizations for Automatic Performance Tuning
Proceedings of the International Symposium on Code Generation and Optimization
On the decidability of phase ordering problem in optimizing compilation
Proceedings of the 3rd conference on Computing frontiers
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
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
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Proceedings of the International Symposium on Code Generation and Optimization
Phase-based adaptive recompilation in a JVM
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
Mapping parallelism to multi-cores: a machine learning based approach
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
Engineering A Compiler
Automatic Feature Generation for Machine Learning Based Optimizing Compilation
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Evaluating iterative optimization across 1000 datasets
PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
Collective optimization: A practical collaborative approach
ACM Transactions on Architecture and Code Optimization (TACO)
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
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The compiler optimizations we enable and the order in which we apply them on a program have a substantial impact on the program execution time. Compilers provide default optimization sequences which can give good program speedup. As the default sequences have to optimize programs with different characteristics, they embed in them multiple subsequences which can optimize different classes of programs. These multiple subsequences may falsely interact with each other and affect the potential program speedup achievable. Instead of searching for a single universally optimal sequence, we can construct a small set of good sequences such that for every program class there exists a near-optimal optimization sequence in the good sequences set. If we can construct such a good sequences set which covers all the program classes in the program space, then we can choose the best sequence for a program by trying all the sequences in the good sequences set. This approach completely circumvents the need to solve the program classification problem. Using a sequence set size of around 10 we got an average speedup up to 14% on PolyBench programs and up to 12% on MiBench programs. Our approach is quite different from either the iterative compilation or machine-learning-based prediction modeling techniques proposed in the literature so far. We use different training and test datasets for cross-validation as against the Leave-One-Out cross-validation technique.