High-level optimization via automated statistical modeling
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Automatic performance tuning of sparse matrix kernels
Automatic performance tuning of sparse matrix kernels
Capturing, indexing, clustering, and retrieving system history
Proceedings of the twentieth ACM symposium on Operating systems principles
HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Proceedings of the International Symposium on Code Generation and Optimization
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Roofline: an insightful visual performance model for multicore architectures
Communications of the ACM - A Direct Path to Dependable Software
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Detecting application-level failures in component-based Internet services
IEEE Transactions on Neural Networks
Vrisha: using scaling properties of parallel programs for bug detection and localization
Proceedings of the 20th international symposium on High performance distributed computing
Dynamic resource tuning for flexible core chip multiprocessors
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
PerfXplain: debugging MapReduce job performance
Proceedings of the VLDB Endowment
Automatic static feature generation for compiler optimization problems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Oversubscription of computational resources on multicore desktop systems
MSEPT'12 Proceedings of the 2012 international conference on Multicore Software Engineering, Performance, and Tools
Mitigating the compiler optimization phase-ordering problem using machine learning
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Starchart: hardware and software optimization using recursive partitioning regression trees
PACT '13 Proceedings of the 22nd international conference on Parallel architectures and compilation techniques
Gunther: search-based auto-tuning of mapreduce
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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Multicore architectures have become so complex and diverse that there is no obvious path to achieving good performance. Hundreds of code transformations, compiler flags, architectural features and optimization parameters result in a search space that can take many machinemonths to explore exhaustively. Inspired by successes in the systems community, we apply state-of-the-art machine learning techniques to explore this space more intelligently. On 7-point and 27-point stencil code, our technique takes about two hours to discover a configuration whose performance is within 1% of and up to 18% better than that achieved by a human expert. This factor of 2000 speedup over manual exploration of the auto-tuning parameter space enables us to explore optimizations that were previously off-limits. We believe the opportunity for using machine learning in multicore autotuning is even more promising than the successes to date in the systems literature.