High-performance computing: the essential tool and the essential challenge

[EN] Prolog to the Journal of Supercomputing, volume 73, issue 1.We would also like to acknowledge to the “Ministerio de Educación y Ciencia” of Spain, for its support to the Spanish CAPAP-H5 network (HPC in Heterogeneous Systems, TIN2014-53522-REDT), and to the “Ministerio de Economía y Competitividad” from Spain/FEDER for supporting Grants TEC2015-67387-C4-1-R and TEC2015-67387-C4-3-R.Alonso-Jordá, P.; Ranilla, J.; Vigo-Aguiar, J. (2017). High-performance computing: the essential tool and the essential challenge. The Journal of Supercomputing. 73(1):1-3. https://doi.org/10.1007/s11227-016-1922-5S1373

JURECA: General-purpose supercomputer at Jülich Supercomputing Centre

JURECA is a petaflop-scale, general-purpose supercomputer operated by Jülich Supercomputing Centre at Forschungszentrum Jülich. Utilizing a flexible cluster architecture based on T-Platforms V-Class blades and a balanced selection of best of its kind components the system supports a wide variety of high-performance computing and data analytics workloads and offers a low entrance barrier for new users.New version available: Jülich Supercomputing Centre. (2018). JURECA: Modular supercomputer at Jülich Supercomputing Centre. Journal of large-scale research facilities, 4, A132. http://dx.doi.org/10.17815/jlsrf-4-121-

Modelling Energy Consumption based on Resource Utilization

Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94\% of accuracy and 6.32 watts of error in the best case.Comment: Submitted to Journal of Supercomputing on 14th June, 201

Quantum Monte Carlo study of inhomogeneous neutron matter

We present an ab-initio study of neutron drops. We use Quantum Monte Carlo techniques to calculate the energy up to 54 neutrons in different external potentials, and we compare the results with Skyrme forces. We also calculate the rms radii and radial densities, and we find that a re-adjustment of the gradient term in Skyrme is needed in order to reproduce the properties of these systems given by the ab-initio calculation. By using the ab-initio results for neutron drops for close- and open-shell configurations, we suggest how to improve Skyrme forces when dealing with systems with large isospin-asymmetries like neutron-rich nuclei.Comment: 8 pages, 6 figures, talk given at Horizons on Innovative Theories, Experiments, and Supercomputing in Nuclear Physics 2012, (HITES2012), New Orleans, Louisiana, June 4-7, 2012; to appear in Journal of Physics: Conference Series (JPCS

A Distributed Economics-based Infrastructure for Utility Computing

Existing attempts at utility computing revolve around two approaches. The first consists of proprietary solutions involving renting time on dedicated utility computing machines. The second requires the use of heavy, monolithic applications that are difficult to deploy, maintain, and use. We propose a distributed, community-oriented approach to utility computing. Our approach provides an infrastructure built on Web Services in which modular components are combined to create a seemingly simple, yet powerful system. The community-oriented nature generates an economic environment which results in fair transactions between consumers and providers of computing cycles while simultaneously encouraging improvements in the infrastructure of the computational grid itself.Comment: 8 pages, 1 figur