References of "Dorronsorro, Bernabé"
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See detailAn LLVM-based Approach to Generate Energy Aware Code by means of MOEAs
Varrette, Sébastien UL; Dorronsorro, Bernabe; Bouvry, Pascal UL

in Proc. of the 7th European Symposium on Computational Intelligence and Mathematics.(ESCIM 2015) (2015, October)

Moderating the energy consumption and building eco-friendly computing infrastructure is of major concerns in the implementation of High Performance Computing (HPC) system, especially when a world- wide ... [more ▼]

Moderating the energy consumption and building eco-friendly computing infrastructure is of major concerns in the implementation of High Performance Computing (HPC) system, especially when a world- wide effort target the production of an Exaflop machine by 2020 within a power envelop of 20 MW. Tracking energy savings can be done at var- ious levels and in this paper, we investigate the automatic generation of energy aware software with the ambition to keep the same level of efficiency, testability, scalability and security. To this end, the Evo-LLVM framework is proposed. Based on the mod- ular LLVM Compiler Infrastructure and exploiting various evolutionary heuristics, our scheme is designed to optimize for a given input source code (written in C) the sequence of LLVM transformations that should be applied to the source code to improve its energy efficiency without degrading its other performance attributes (execution time, parallel or distributed scalability). Measuring this capacity is based on the combi- nation of several metrics optimized simultaneously with Multi-Objective Evolutionary Algorithms (MOEAs). In this position paper, the NSGA- II algorithm is implemented within the Evo-LLVM yet the analysis of more advanced heuristics is in progress. In all cases, the experimental validation of the framework over a pedagogical code sample reveal a drastic improvement of the energy consumed during the execution while maintaining (or even improving) the average execution time. [less ▲]

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See detailDistributed Cellular Evolutionary Algorithms in a Byzantine Environment
Muszynski, Jakub UL; Varrette, Sébastien UL; Dorronsorro, Bernabé et al

in Proc. of the 18th Intl. Workshop on Nature Inspired Distributed Computing (NIDISC 2015), part of the 29th IEEE/ACM Intl. Parallel and Distributed Processing Symposium (IPDPS 2015) (2015, May)

Distributed parallel computing platforms contribute for a large part to some of the most powerful computers. Such architec- tures are typically based on accelerators (General Purpose com- puting on ... [more ▼]

Distributed parallel computing platforms contribute for a large part to some of the most powerful computers. Such architec- tures are typically based on accelerators (General Purpose com- puting on Graphics Processing Units, Many Integrated Cores e.g Xeon Phi co-processors) and/or a large number of interconnected computing nodes. Obviously, they raise new challenges, typically in terms of scalability, robustness, adaptability and security. At the advent of the quest for Ultrascale Computing Systems, this paper addresses the issue of fault tolerance toward Byzantine failures overs such platforms. Indeed, the inherent unpredictable nature of these errors render their detection, not speaking of their correction, hard or even impossible to perform at large-scale. At this level, Algorithm-Based Fault Tolerance (ABFT) techniques where the fault tolerance scheme is tailored to the algorithm performed, seems the most promising approaches to deal with such failures. In this context, Evolutionary Algorithms (EAs), especially panmictic global parallel EAs, exhibit a remarkable resilience against byzantine failures modeled as cheating faults as demonstrated either empirically or theoretically in previous studies [1], [2]. In this paper, we extend this analysis to the case of distributed EAs based on the cellular model leading to distributed Cellular Evolutionary Algorithms (dCEAs). Our empirical study over a set or reference optimization problem confirm the ABFT nature of dCEAs. To our knowledge, this is the first study of dCEAs under the perspective of cheating issues and crash faults in a domain of distributed computations, thus opening new insights and perspectives for the design of competitive ultra-scale system based on evolutionary programming models. [less ▲]

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