By Andreas Heinig, Vincent J. Mooney, Florian Schmoll, Peter Marwedel, Krishna Palem (auth.), Andreas Herkersdorf, Kay Römer, Uwe Brinkschulte (eds.)
This publication constitutes the refereed complaints of the twenty fifth foreign convention on structure of Computing platforms, ARCS 2012, held in Munich, Germany, in February/March 2012. The 20 revised complete papers offered in 7 technical classes have been conscientiously reviewed and chosen from sixty five submissions. The papers are equipped in topical sections on robustness and fault tolerance, power-aware processing, parallel processing, processor cores, optimization, and conversation and memory.
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Additional info for Architecture of Computing Systems – ARCS 2012: 25th International Conference, Munich, Germany, February 28 - March 2, 2012. Proceedings
However, these approaches may illustrate scalability issues when the number of cores increases or when an unbalanced workload exists. To overcome these issues, we present in this paper a dynamic task level redundancy technique for fault tolerant OpenMP applications. Our method is based on dynamically applying a Triple Modular Redundancy for OpenMP tasks through a dedicated runtime and on applying a majority voting to guarantee correct results. Our ﬂexible fault tolerant OpenMP approach has been evaluated for performance and fault coverage and it showed small overhead with good error detection and recovery rate.
This methodology adopts approximately-timed task-accurate performance models and augments them with power-state-based power models to enable eﬃcient simulation. A ﬂexible method is also proposed to model complex dynamic power management policies so that their eﬀects can be evaluated. We validate the accuracy of our methodology by comparing simulation results with measurements on a real mobile phone platform. Experimental results show that the simulated power proﬁle matches very well with the measurements and it only takes about 100 ms to simulate a 20 ms GSM paging burst use case.
Events Application Annotations Behavior PSM DPMP Power & Performance Monitor Model MPA (XML) Mapping Info. Mapping Resource Resource Resource Power & Performance Evaluation Results 40 Fig. 1. Components of our methodology Our methodology is composed of four parts: behavior models, resource models, Mapping & Parameter Annotations (MPA) and a Power & Performance Monitor (PPM), as shown in Fig. 1. The behavior models, including power behavior models, environment event models and application behavior models represent the system power and functionality behavior.