C4.5: programs for machine learning
C4.5: programs for machine learning
A tool framework for static and dynamic analysis of object-oriented software with templates
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
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
Predicting Unroll Factors Using Supervised Classification
Proceedings of the international symposium on Code generation and optimization
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
Design and Implementation of a Parallel Performance Data Management Framework
ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
PerfExplorer: A Performance Data Mining Framework For Large-Scale Parallel Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Proceedings of the International Symposium on Code Generation and Optimization
Optimizing MPI Runtime Parameter Settings by Using Machine Learning
Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Proceedings of the 24th ACM International Conference on Supercomputing
Speeding up Nek5000 with autotuning and specialization
Proceedings of the 24th ACM International Conference on Supercomputing
A programming language interface to describe transformations and code generation
LCPC'10 Proceedings of the 23rd international conference on Languages and compilers for parallel computing
Auto-tuning full applications: A case study
International Journal of High Performance Computing Applications
Online Adaptive Code Generation and Tuning
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Effective source-to-source outlining to support whole program empirical optimization
LCPC'09 Proceedings of the 22nd international conference on Languages and Compilers for Parallel Computing
Tuning parallel applications in parallel
Tuning parallel applications in parallel
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The process of empirical autotuning results in the generation of many code variants which are tested, found to be suboptimal, and discarded. By retaining annotated performance profiles of each variant tested over the course of many autotuning runs of the same code across different hardware environments and different input datasets, we can apply machine learning algorithms to generate classifiers for runtime selection of code variants from a library, generate specialized variants, and potentially speed the process of autotuning by starting the search from a point predicted to be close to optimal. In this paper, we show how the TAU Performance System suite of tools can be applied to autotuning to enable reuse of performance data generated through autotuning.