Parallel Parameter Tuning for Applications with Performance Variability
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Adaptive scheduling with parallelism feedback
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Automatic software interference detection in parallel applications
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Enhancing operating system support for multicore processors by using hardware performance monitoring
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Annotation-based empirical performance tuning using Orio
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Tuning parallel applications in parallel
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Contention aware execution: online contention detection and response
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Hybrid optimizations: which optimization algorithm to use?
CC'06 Proceedings of the 15th international conference on Compiler Construction
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Multicore hardware and system software have become complex and differ from platform to platform. Parallel application performance optimization and portability are now a real challenge. In practice, the effects of tuning parameters are hard to predict. Programmers face even more difficulties when several applications run in parallel and influence each other indirectly. We tackle these problems with Perpetuum, a novel operating-system-based auto-tuner that is capable of tuning applications while they are running. We go beyond tuning one application in isolation and are the first to employ OS-based auto-tuning to improve system-wide application performance. Our fully functional autotuner extends the Linux kernel, and the application tuning process does not require any user involvement. General multicore applications are automatically re-tuned on new platforms while they are executing, which makes portability easy. Extensive case studies with real applications demonstrate the feasibility and efficiency of our approach. Perpetuum realizes a first milestone in our vision to make every performance-critical multicore application auto-tuned by default.