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

Firefly algorithm for polynomial Bézier surface parameterization

A classical issue in many applied fields is to obtain an approximating surface to a given set of data points. This problem arises in Computer-Aided Design and Manufacturing (CAD/CAM), virtual reality, medical imaging, computer graphics, computer animation, and many others. Very often, the preferred approximating surface is polynomial, usually described in parametric form. This leads to the problem of determining suitable parametric values for the data points, the so-called surface parameterization. In real-world settings, data points are generally irregularly sampled and subjected to measurement noise, leading to a very difficult nonlinear continuous optimization problem, unsolvable with standard optimization techniques. This paper solves the parameterization problem for polynomial Bézier surfaces by applying the firefly algorithm, a powerful nature-inspired metaheuristic algorithm introduced recently to address difficult optimization problems. The method has been successfully applied to some illustrative examples of open and closed surfaces, including shapes with singularities. Our results show that the method performs very well, being able to yield the best approximating surface with a high degree of accuracy

Simulating the Behaviour of the Human Brain on NVIDIA GPU: cuHinesBatch & cuThomasBatch implementations

Understand the human brain is one of the century challenges. On this work we are going to achieve a small step towards this objective presenting a novel data layout in order to compute more efficiently the Hines algorithm on GPU. A more general tridiagonal solver is going to be presented too

Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation

Urban regeneration in the digital era: how to develop Smart City strategies in large european cities. La rigenerazione urbana nell’era digitale: come sviluppare strategie Smart City in città europee di grandi dimensioni.

La ricerca documentata in questo articolo è stata svolta allo scopo di approfondire la conoscenza relativa ai processi di sviluppo delle strategie che consentono alle città di diventare Smart. Per raggiungere questo obiettivo sono state analizzate le strategie proposte dalle amministrazioni comunali di Amsterdam e Barcellona. Due casi di successo che hanno permesso di delineare una step-by step roadmap in cui è stato descritto un possibile approccio per costruire strategie Smart City in città Europee di grandi dimensioni. Nonostante il suo stadio di sviluppo iniziale, questa procedura fornisce nuova conoscenza, prospettive di ricerca innovative, e un modello concettuale per sostenere lo svolgimento di ulteriori ricerche comparative. Un’attività indispensabile per garantire la sua continua crescita e il perfezionamento della sua struttura.The study documented in this paper has been carried out in order to acquire new knowledge concerning the development processes of smart city strategies. This aim has been achieved through the analysis of the initiatives proposed by the municipal administrations of Amsterdam and Barcelona. Two successful cases that have allowed to outline a step-by-step roadmap in which a possible approach for developing smart city strategies in large European cities is described. Despite its early stage of development, this procedure provides new knowledge, innovative research perspectives, and a conceptual framework for supporting future comparative research and theory-building. Activities that are fundamental to ensure its continuous growth and the refinement of its structure

Urban regeneration in the digital era: how to develop smart city strategies in large European cities

The study documented in this paper has been carried out in order toacquire new knowledge concerning the development processes of smart city strategies. This aim has been achieved through the analysis of the initiatives proposed by the municipal administrations of Amsterdam and Barcelona. Two successful cases that have allowed to outline a step-by-step roadmap in which a possible approach for developing smart city strategies in large European cities is described. Despite its early stage of development, this procedure provides new knowledge, innovative research perspectives, and a conceptual framework for supporting future comparative research and theory building.Activities that are fundamental to ensure its continuous growth and the refinement of its structure

New Metropolitan Perspectives

​This open access book presents the outcomes of the symposium “NEW METROPOLITAN PERSPECTIVES,” held at Mediterranea University, Reggio Calabria, Italy on May 26–28, 2020. Addressing the challenge of Knowledge Dynamics and Innovation-driven Policies Towards Urban and Regional Transition, the book presents a multi-disciplinary debate on the new frontiers of strategic and spatial planning, economic programs and decision support tools in connection with urban–rural area networks and metropolitan centers. The respective papers focus on six major tracks: Innovation dynamics, smart cities and ICT; Urban regeneration, community-led practices and PPP; Local development, inland and urban areas in territorial cohesion strategies; Mobility, accessibility and infrastructures; Heritage, landscape and identity;and Risk management,environment and energy. The book also includes a Special Section on Rhegion United Nations 2020-2030. Given its scope, the book will benefit all researchers, practitioners and policymakers interested in issues concerning metropolitan and marginal areas