Managing multi-configuration hardware via dynamic working set analysis
ISCA '02 Proceedings of the 29th annual international symposium on Computer architecture
Single-ISA Heterogeneous Multi-Core Architectures: The Potential for Processor Power Reduction
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Single-ISA Heterogeneous Multi-Core Architectures for Multithreaded Workload Performance
Proceedings of the 31st annual international symposium on Computer architecture
Dynamic thread assignment on heterogeneous multiprocessor architectures
Proceedings of the 3rd conference on Computing frontiers
Bias scheduling in heterogeneous multi-core architectures
Proceedings of the 5th European conference on Computer systems
A comprehensive scheduler for asymmetric multicore systems
Proceedings of the 5th European conference on Computer systems
Phase-Guided Scheduling on Single-ISA Heterogeneous Multicore Processors
DSD '11 Proceedings of the 2011 14th Euromicro Conference on Digital System Design
Phase-based scheduling and thread migration for heterogeneous multicore processors
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
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Heterogeneous multicore processors (HMPs) offer promise for significant efficiency improvement. Power-effcient cores can be paired with higher performance cores in an HMP to achieve a beneficial design in terms of both power and performance. However, such processors produce challenges in the effective mapping of threads to cores. An application could have very different behavior and performance when executing on cores of different types. The behavior of typical applications also vary with their phases of execution. Thus, the type of core providing the best performance for an application may depend on the current phase. In this work, we highlight the correlation between execution phases of an application and the performance of those phases on particular core types. We propose mechanisms that identify program phases, exploit the performance behavior of these phases to make effective scheduling decisions, and reuse the result of these scheduling decisions on future occurrences of the same program phases.