Machine Learning
Machine Learning
Finding effective optimization phase sequences
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Exploring the structure of the space of compilation sequences using randomized search algorithms
The Journal of Supercomputing
Automating the construction of compiler heuristics using machine learning
Automating the construction of compiler heuristics using machine learning
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)
System-level monitoring of floating-point performance to improve effective system utilization
State of the Practice Reports
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In this paper we show a machine learning based implementation of autotuning, built with the cTuning CC framework. We implemented the PGI compiler in the cTuning CC framework, plugged in a few additional benchmarks and tested it on a Cray XT5m supercomputer. The main contribution of the present paper consists in combining existing autotuning techniques and using them with the PGI production compiler. Although not ready for production workflows yet, our results are encouraging.