A Model For Speedup of Parallel Programs
A Model For Speedup of Parallel Programs
An Adaptive Strategy for Resource Allocation Modeled as Minority Game
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
Thousand core chips: a technology perspective
Proceedings of the 44th annual Design Automation Conference
ADAM: run-time agent-based distributed application mapping for on-chip communication
Proceedings of the 45th annual Design Automation Conference
DistRM: distributed resource management for on-chip many-core systems
CODES+ISSS '11 Proceedings of the seventh IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Energy efficient frequency scaling and scheduling for malleable tasks
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Run-time adaption for highly-complex multi-core systems
Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis
Agent-based distributed power management for kilo-core processors
Proceedings of the International Conference on Computer-Aided Design
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Many-core architectures used in embedded systems will contain hundreds of processors in the near future. Already now, it is necessary to study how to manage such systems when dynamically scheduling applications with different phases of parallelism and resource demands. A recent research area called invasive computing proposes a decentralized workload management scheme of such systems: applications may dynamically claim additional processors during execution and release these again, respectively. In this paper, we study how to apply the concepts of invasive computing for realizing decentralized core allocation schemes in homogeneous many-core systems with the goal of maximizing the average speedup of running applications at any point in time. A theoretical analysis based on game theory shows that it is possible to define a core allocation scheme that uses local information exchange between applications only, but is still able to provably converge to optimal results. The experimental evaluation demonstrates that this allocation scheme reduces the overhead in terms of exchanged messages by up to 61.4% and even the convergence time by up to 13.4% compared to an allocation scheme where all applications exchange information globally with each other.