Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Automatically tuned linear algebra software
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
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
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
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
Fast searches for effective optimization phase sequences
Proceedings of the ACM SIGPLAN 2004 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
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
Automatic Selection of Compiler Options Using Non-parametric Inferential Statistics
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
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
Online performance auditing: using hot optimizations without getting burned
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
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
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Proceedings of the International Symposium on Code Generation and Optimization
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
HPCTOOLKIT: tools for performance analysis of optimized parallel programs http://hpctoolkit.org
Concurrency and Computation: Practice & Experience - Scalable Tools for High-End Computing
Continuous learning of compiler heuristics
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
Finding good optimization sequences covering program space
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
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Iterative compilation techniques, which involve iterating over different sets of optimizations, have proven useful in helping compilers choose the right set of optimizations for a given program. However, compilers typically have a large number of optimizations to choose from, making it impossible to iterate over a significant fraction of the entire optimization search space. Recent research has proposed to "intelligently" iterate over the optimization search space using predictive methods. In particular, state-the-art methods in iterative compilation use characteristics of the code being optimized to predict good optimization sequences to evaluate. Thus, an important step in developing predictive methods for compilation is deciding how to model the problem of choosing the right optimizations. In this paper, we evaluate three different ways of modeling the problem of choosing the right optimization sequences using machine learning techniques. We evaluate a novel prediction modeling technique, namely a tournament predictor, that is able to effectively predict good optimization sequences. We show that our tournament predictor can outperform current state-of-the-art predictors and the most aggressive setting of the Open64 compiler (-Ofast) on an average by 75% in just 10 iterations over a set of embedded and scientific kernels. Moreover, using our tournament predictor, we achieved on average 10% improvement over -Ofast for a set of MiBench applications.