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A scalable monitoring infrastructure for self-organizing many-core architectures

A scalable monitoring infrastructure for self-organizing man...

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15th Euromicro Conference on Digital System Design, DSD 2012

作者： Kramer, David Karl, Wolfgang Department of Computer Architecture and Parallel Processing 76128 Karlsruhe Germany

ISBN: (纸本)9780769547985

Self-organizing principles can address the growing complexity and the huge challenge of management and efficient utilization of adaptive many-core architectures. Fundamental for realizing a self-organizing behavior within such architectures is a dedicated monitoring infrastructure that provides the essential information about the system status and system behavior for realizing the basic property of self-awareness. This paper therefore proposes a flexible, hierarchical and scalable monitoring infrastructure for self-organizing, adaptive many-core architectures. The employed basic monitoring unit in the bottom monitoring layer performs data aggregation and filtering and reduces the amount of data that must be processed in higher monitoring layers. The middle layer performs first data analysis and is further responsible for hiding the heterogeneity of the underlying hardware configuration to the topmost monitoring layer. The latter is finally responsible for detecting changes in the system behavior and realizing self-awareness. The proposed monitoring infrastructure was evaluated entirely using a simulation framework. Results show that the infrastructure is able of detecting changes in the system behavior of an entire many-core system causing only a minor system disturbance. Further, the prototypical implementation of the basic monitoring unit proved that it can be realized very efficiently in hardware. © 2012 IEEE.

关键词： Monitoring

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Realizing a proactive, self-optimizing system behavior within adaptive, heterogeneous many-core architectures

Realizing a proactive, self-optimizing system behavior withi...

引用

2012 IEEE 6th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2012

作者： Kramer, David Karl, Wolfgang Department of Computer Architecture and Parallel Processing 76131 Karlsruhe Germany

ISBN: (纸本)9780769548517

For maintaining high performance and minimizing power consumption, adaptive, heterogeneous many-core architectures can be adapted at runtime to changing environmental requests or conditions as well as to changes resulting from the dynamics of the workload itself. However, the huge complexity of such architectures makes their optimization very challenging at runtime. This challenge is therefore addressed within this paper by an Organic Computing approach for realizing a proactive, self-optimizing system behavior within adaptive, heterogeneous systems using a light-weight Learning Classifier System and a Run Length Encoding Markov predictor. The first realizes a self-optimizing behavior, freeing the user from the burden of optimizing the system manually, and the latter captures the system behavior, permits prediction of future system states, and therefore permits exploiting regular behavior for further improving the overall system performance. Using the use case of optimizing the overall system performance, results showed that the proactive, self-optimizing system achieved a performance improvement of 11.3\% in comparison to a non-optimizing system. © 2012 IEEE.

关键词： Reinforcement learning

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Cost-aware function migration in heterogeneous systems 11

Cost-aware function migration in heterogeneous systems

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Proceedings of the 6th International Conference on High Performance and Embedded architectures and Compilers

作者： Kicherer, Mario Buchty, Rainer Karl, Wolfgang Department Computer Architecture and Parallel Processing Karlsruhe Institute of Technology 76128 Karlsruhe Germany

ISBN: (纸本)9781450302418

Today's approaches towards heterogeneous computing rely on either the programmer or dedicated programming models to efficiently integrate heterogeneous components. In this work, we propose an adaptive cost-aware function-migration mechanism built on top of a light-weight hardware abstraction layer. With this mechanism, the highly dynamic task of choosing the most beneficial processing unit will be hidden from the programmer while causing only minor variation in the work and program flow. The migration mechanism transparently adapts to the current workload and system environment without the necessity of JIT compilation or binary translation. Evaluation shows that our approach successfully adapts to new circumstances and predicts the most beneficial processing unit (PU). Through fine-grained PU selection, our solution achieves a speedup of up to 2.27 for the average kernel execution time but introduces only a marginal overhead in case its services are not required. Copyright 2011 ACM.

关键词： Abstracting

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