References of "Georgatos, Fotis 40021501"
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See detailTen simple rules for organizing an unconference.
Budd, Aidan; Dinkel, Holger; Corpas, Manuel et al

in PLoS Computational Biology (2015), 11(1), 1003905

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See detailManagement of an Academic HPC Cluster: The UL Experience
Varrette, Sébastien UL; Bouvry, Pascal UL; Cartiaux, Hyacinthe UL et al

in Proc. of the 2014 Intl. Conf. on High Performance Computing Simulation (HPCS 2014) (2014, July)

The intensive growth of processing power, data storage and transmission capabilities has revolutionized many aspects of science. These resources are essential to achieve high-quality results in many ... [more ▼]

The intensive growth of processing power, data storage and transmission capabilities has revolutionized many aspects of science. These resources are essential to achieve high-quality results in many application areas. In this context, the University of Luxembourg (UL) operates since 2007 an High Performance Computing (HPC) facility and the related storage. The aspect of bridging computing and storage is a requirement of UL service – the reasons are both legal (certain data may not move) and performance related. Nowa- days, people from the three faculties and/or the two Interdisciplinary centers within the UL, are users of this facility. More specifically, key research priorities such as Systems Bio-medicine (by LCSB) and Security, Reliability & Trust (by SnT) require access to such HPC facilities in order to function in an adequate environment. The management of HPC solutions is a complex enterprise and a constant area for discussion and improvement. The UL HPC facility and the derived deployed services is a complex computing system to manage by its scale: at the moment of writing, it consists of 150 servers, 368 nodes (3880 computing cores) and 1996 TB of shared raw storage which are all configured, monitored and operated by three per- sons using advanced IT automation solutions based on Puppet [1], FAI [2] and Capistrano [3]. This paper covers all the aspects in relation to the management of such a complex infrastructure, whether technical or administrative. Most design choices or implemented approaches have been motivated by several years of experience in addressing research needs, mainly in the HPC area but also in complementary services (typically Web-based). In this context, we tried to answer in a flexible and convenient way many technological issues. This experience report may be of interest for other research centers belonging either to the public or the private sector looking for good if not best practices in cluster architecture and management. [less ▲]

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See detailPerformance tuning of applications for HPC systems employing Simulated Annealing optimization
Plugaru, Valentin UL; Georgatos, Fotis UL; Varrette, Sébastien UL et al

Report (2013)

Building fast software in an HPC environment raises great challenges as software used for simulation and modelling is generally complex and has many dependencies. Current approaches involve manual tuning ... [more ▼]

Building fast software in an HPC environment raises great challenges as software used for simulation and modelling is generally complex and has many dependencies. Current approaches involve manual tuning of compilation parameters in order to minimize the run time, based on a set of predefined defaults, but such an approach involves expert knowledge, is not scalable and can be very expensive in person-hours. In this paper we propose and develop a modular framework called POHPC that uses the Simulated Annealing meta-heuristic algorithm to automatically search for the optimal set of library options and compilation flags that can give the best execution time for a library-application pair on a selected hardware architecture. The framework can be used in modern HPC clusters using a variety of batch scheduling systems as execution backends for the optimization runs, and will discover optimal combinations as well as invalid sets of options and flags that result in failed builds or application crashes. We demonstrate the optimization of the FFTW library working in conjunction with the high- profile community codes GROMACS and QuantumESPRESSO, whereby the suitability of the technique is validated. [less ▲]

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See detailComputational infrastructures for data and knowledge management in systems biology
Georgatos, Fotis UL; Ballereau, S.; Pellet, J. et al

in Prokop, Ales; Csukás, Bela (Eds.) Systems Biology: Integrative Biology and Simulation Tools (2013)

The volume, complexity and heterogeneity of data originating from high throughput functional genomics technologies have created challenges and opportunities for Information Technology (IT) departments ... [more ▼]

The volume, complexity and heterogeneity of data originating from high throughput functional genomics technologies have created challenges and opportunities for Information Technology (IT) departments. These increased demands have also led to increasing costs for IT infrastructure such as necessary computing power and storage devices, as well as further costs for manpower effort, required for maintenance. This chapter describes some of the challenges for computational analysis infrastructure, including bottlenecks and most pressing needs that have to be addressed to effectively support the development of systems biology and its application in medicine. [less ▲]

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See detailA Grid-enabled CPU scavenging architecture and a case study of its use in Greek School Network
Georgatos, Fotis UL; Gkamas, Vasileios; Ilias, Aristeidis et al

in Journal of Grid Computing (2010), 8(1), 61-75

In this paper we present a CPU scavenging architecture suitable for desktop resources, and we study its appropriateness in exploiting the PC Laboratory resources of the Greek School Network and their ... [more ▼]

In this paper we present a CPU scavenging architecture suitable for desktop resources, and we study its appropriateness in exploiting the PC Laboratory resources of the Greek School Network and their integration to the existing HellasGrid national infrastructure. School laboratories form an extensive network equipped with computational systems and fast Internet connections. As this infrastructure is utilized at most 8 h per day and 5 days per week, it could be made available during its remaining idle time for computational purposes through the use of Grid technology. The structure and organization of the school laboratories and backbone network enables the CPU scavenging service, as an independent and additional service, which will not violate the operational rules and policies of the school network, while it will add additional resources to the current HellasGrid infrastructure with low adaptation cost. [less ▲]

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