Simultaneous multithreading: maximizing on-chip parallelism
ISCA '95 Proceedings of the 22nd annual international symposium on Computer architecture
Continuous profiling: where have all the cycles gone?
Proceedings of the sixteenth ACM symposium on Operating systems principles
System support for automatic profiling and optimization
Proceedings of the sixteenth ACM symposium on Operating systems principles
ProfileMe: hardware support for instruction-level profiling on out-of-order processors
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Progress-based regulation of low-importance processes
Proceedings of the seventeenth ACM symposium on Operating systems principles
Symbiotic jobscheduling for a simultaneous multithreaded processor
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
SEDA: an architecture for well-conditioned, scalable internet services
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
MAGNET: A Tool for Debugging, Analyzing and Adapting Computing Systems
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Adaptive overload control for busy internet servers
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Session state: beyond soft state
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
An early prototype of an autonomic performance environment for exascale
Proceedings of the 3rd International Workshop on Runtime and Operating Systems for Supercomputers
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We argue that systems software can exploit hard-ware instrumentation mechanisms, such as performance monitoring counters in modern processors, along with general system statistics to reactively modify its behavior to achieve better performance. In this paper we outline our approach of using these instrumentation mechanisms to estimate productivity and overhead metrics while running user applications. At the kernel level, we speculate that the scheduler can exploit these metrics to improve system performance. At the application level, we show that applications can use these metrics as well as application-specific productivity metrics to reactively tune their performance. We give several examples of using reflection at the kernel level (e.g., scheduling to improve memory hierarchy performance) and at the application level (e.g., server throttling).