Las bibliotecas de dos alfaquíes borjanos.

Labarta Gomez, Ana Maria - [email protected]

Power-law decay in first-order relaxation processes

Starting from a simple definition of stationary regime in first-order relaxation processes, we obtain that experimental results are to be fitted to a power-law when approaching the stationary limit. On the basis of this result we propose a graphical representation that allows the discrimination between power-law and stretched exponential time decays. Examples of fittings of magnetic, dielectric and simulated relaxation data support the results.Comment: to appear in Phys. Rev. B; 4 figure

Carvajal y la voluntad de ser arquitecto: La construcción del proyecto y la belleza eficaz

Javier Carvajal Ferrer (Barcelona 1926–Madrid 2013) aúna magisterio y praxis de tal modo que es imposible acercarse a su obra sin tener en cuenta su docencia y, al revés, no puede entenderse su discurso en las aulas si, a la vez, no se transita por sus proyectos. Más aún, para Carvajal el proyecto arquitectónico es el ámbito específico de la investigación del profesor y el mecanismo de eficacia por el que el profesional debe dar una respuesta coherente a la teoría y a la praxis. Este texto pretende evocar algunas de las virtudes de su obra arquitectónica y de las enseñanzas de su labor docente, ejemplificadas en dos de sus proyectos más emblemáticos. Uno, su proyecto más alabado, el Pabellón de España para la Feria Mundial de Nueva York (1964–65) del que se cumplen cincuenta años. El otro, el más polémico, la Torre de Valencia en Madrid (1970–1973). Y hacerlo investigando sobre el rigor y la belleza de unos planos que él mismo dibujaba. Ambas obras ilustran, además, aspectos relevantes de su propia personalidad que inevitablemente se imbrican en su febril actividad como arquitecto y docente, y muestran cómo su firme confianza en la calidad del proyecto sigue siendo la mejor contribución a la evolución de la arquitectura. Autenticidad, vocación, voluntad, exigencia, precisión, rigor, perfección, coherencia o ilusión, se resumen en uno de sus célebres mensajes a los alumnos: “Sueñen con lo posible”.Javier Carvajal Ferrer (Barcelona, 1926–Madrid, 2013) combined teaching and practice so that it is impossible to approach his work without considering his teaching and, conversely, we cannot understand his speech in classrooms if, at the same time, we do not examine his projects. Moreover, for Carvajal the architectural project is the specific field of the professor’s research and the efficacy mechanism by which the practitioner must give a coherent response to both theory and praxis. This text aims to evoke some of the virtues of his architectural work and the lessons of his teaching, exemplified in two of his most emblematic projects: first, his most praised project, the Spanish Pavilion for the World’s Fair in New York (1964–65), which marks fifty years; and the other project—more controversial—the Valencia Tower in Madrid (1970–73) by researching the rigor and beauty of the plans that he drew himself. Both works also illustrate important aspects of his personality that inevitably overlap in his feverish activity as an architect and professor, and show how his strong confidence in the quality of the project remains as the best contribution to the evolution of architecture. Authenticity, vocation, will, dedication, precision, rigor, perfection, consistency, and illusion are summarized in one of his famous messages to students: “Dream of the possible”

Simulating the behavior of the human brain on GPUS

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 ReviewedPostprint (published version