Suchergebnisse

The simulation of the behavior of the Human Brain is one of the most important challenges in computing today. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulations need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Voltage on neurons' morphology. This is carried out using the Hines Algorithm and, although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. Two different approaches are studied, one for mono-morphology simulations (batch of neurons with the same shape) and one for multi-morphology simulations (batch of neurons where every neuron has a different shape). In mono-morphology simulations we obtain a good performance using just a single kernel to compute all the neurons. However this turns out to be inefficient on multi-morphology simulations. Unlike the previous scenario, in multi-morphology simulations a much more complex implementation is necessary to obtain a good performance. In this case, we must execute more than one single GPU kernel. In every execution (kernel call) one specific part of the batch of the neurons is solved. These parts can be seen as multiple and independent tridiagonal systems. Although the present paper is focused on the simulation of the behavior of the Human Brain, some of these techniques, in particular those related to the solving of tridiagonal systems, can be also used for multiple oil and gas simulations. Our studies have proven that the optimizations proposed in the present work can achieve high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two NVIDIA K80 GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling, even when dealing with a very high number of neurons. ; This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1), from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015-65316-P), the Departament d'Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d'Execució Parallels (2014-SGR-1051). We thank the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence, and the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement No. 749516. ; Peer Reviewed ; Postprint (published version)

WOS: 000414662500008 ; PubMed: 29118316 ; Campylobacter fetus is a venereal pathogen of cattle and sheep, and an opportunistic human pathogen. It is often assumed that C. fetus infection occurs in humans as a zoonosis through food chain transmission. Here we show that mammalian C. fetus consists of distinct evolutionary lineages, primarily associated with either human or bovine hosts. We use whole-genome phylogenetics on 182 strains from 17 countries to provide evidence that C. fetus may have originated in humans around 10,500 years ago and may have "jumped" into cattle during the livestock domestication period. We detect C. fetus genomes in 8% of healthy human fecal metagenomes, where the human-associated lineages are the dominant type (78%). Thus, our work suggests that C. fetus is an unappreciated human intestinal pathobiont likely spread by human to human transmission. This genome-based evolutionary framework will facilitate C. fetus epidemiology research and the development of improved molecular diagnostics and prevention schemes for this neglected pathogen. ; Comision Sectorial de Investigacion Cientifica (CSIC, Uruguay)Consejo Superior de Investigaciones Cientificas (CSIC); Agencia Nacional de Investigacion e Innovacion (ANII, Uruguay) [FSSA_X_2014_1_105252]; Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1091097]; Victorian Government; Fondo de Convergencia Estructural del Mercosur (FOCEM) [COF 03/11]; Wellcome TrustWellcome Trust [098051]; Medical Research Council UKMedical Research Council UK (MRC) [PF451] ; We acknowledge the Pathogen Informatics and Sequencing groups at the Wellcome Trust Sanger Institute for technical support. We also thank to Mark Stares and Hilary Browne at the Host-Microbiota Interactions Laboratory, Wellcome Trust Sanger Institute, for their technical support. G.I. is supported by the Comision Sectorial de Investigacion Cientifica (CSIC, Uruguay) and by the Agencia Nacional de Investigacion e Innovacion (ANII, Uruguay) grant FSSA_X_2014_1_105252. S.C.F. is supported by the Australian National Health and Medical Research Council (1091097) and the Victorian Government's Operational Infrastructure Support Program. This work received financial support from Fondo de Convergencia Estructural del Mercosur (FOCEM) grant COF 03/11, the Wellcome Trust grant number 098051 and the Medical Research Council UK grant number PF451.