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

Predicting performance difficulty from piano sheet music images

Estimating the performance difficulty of a musical score is crucial in music education for adequately designing the learning curriculum of the students. Although the Music Information Retrieval community has recently shown interest in this task, existing approaches mainly use machine-readable scores, leaving the broader case of sheet music images unaddressed. Based on previous works involving sheet music images, we use a mid-level representation, bootleg score, describing notehead positions relative to staff lines coupled with a transformer model. This architecture is adapted to our task by introducing an encoding scheme that reduces the encoded sequence length to one-eighth of the original size. In terms of evaluation, we consider five datasets -- more than 7500 scores with up to 9 difficulty levels -- , two of them particularly compiled for this work. The results obtained when pretraining the scheme on the IMSLP corpus and fine-tuning it on the considered datasets prove the proposal's validity, achieving the best-performing model with a balanced accuracy of 40.34\% and a mean square error of 1.33. Finally, we provide access to our code, data, and models for transparency and reproducibility

A finite strain, finite band method for modeling ductile fracture

We present a finite deformation generalization of the finite thickness embedded discontinuity formulation presented in our previous paper [A.E. Huespe, A. Needleman, J. Oliver, P.J. Sánchez, A finite thickness band method for ductile fracture analysis, Int. J. Plasticity 25 (2009) 2349–2365]. In this framework the transition from a weak discontinuity to a strong discontinuity can occur using a single constitutive relation which is of importance in a range of applications, in particular ductile fracture, where localization typically precedes the creation of new free surface. An embedded weak discontinuity is introduced when the loss of ellipticity condition is met. The resulting localized deformation band is given a specified thickness which introduces a length scale thus providing a regularization of the post-localization response. The methodology is illustrated through several example problems emphasizing finite deformation effects including the development of a cup-cone failure in round bar tension.A.E.H. and P.J.S. are grateful for financial support from ANPCyT and CONICET of Argentina through grants: PICT 2006-01232, PICT 2008-1228 and PIP 112-200901-00341. J.O. is grateful for financial support from the Spanish Ministry of Science and Innovation and the Catalan Government Research Department, under grants BIA2008-00411 and 2009 SGR 1510, respectively.Peer ReviewedPostprint (author's final draft

Nitrate addition to groundwater impacted by ethanol-blended fuel accelerates ethanol removal and mitigates the associated metabolic flux dilution and inhibition of BTEX biodegradation

A comparison of two controlled ethanol-blended fuel releases under monitored natural attenuation (MNA) versus nitrate biostimulation (NB) illustrates the potential benefits of augmenting the electron acceptor pool with nitrate to accelerate ethanol removal and thus mitigate its inhibitory effects on BTEX biodegradation. Groundwater concentrations of ethanol and BTEX were measured 2 m downgradient of the source zones. In both field experiments, initial source-zone BTEX concentrations represented less than 5% of the dissolved total organic carbon (TOC) associated with the release, and measurable BTEX degradation occurred only after the ethanol fraction in the multicomponent substrate mixture decreased sharply. However, ethanol removal was faster in the nitrate amended plot (1.4 years) than under natural attenuation conditions (3.0 years), which led to faster BTEX degradation. This reflects, in part, that an abundant substrate (ethanol) can dilute the metabolic flux of target pollutants (BTEX) whose biodegradation rate eventually increases with its relative abundance after ethanol is preferentially consumed. The fate and transport of ethanol and benzene were accurately simulated in both releases using RT3D with our general substrate interaction module (GSIM) that considers metabolic flux dilution. Since source zone benzene concentrations are relatively low compared to those of ethanol (or its degradation byproduct, acetate), our simulations imply that the initial focus of cleanup efforts (after free-product recovery) should be to stimulate the degradation of ethanol (e.g., by nitrate addition) to decrease its fraction in the mixture and speed up BTEX biodegradation.Petróleo Brasileiro S/A — PETROBRASCoordination of Improvement of Higher Education Personnel (CAPES)National Council for Scientific and Technological Development (CNPq

Density-Dependent Prevalence of Francisella tularensis in Fluctuating Vole Populations, Northwestern Spain

Self Archiving; https://wwwnc.cdc.gov/eid/page/copyright-and-disclaimers J.J.L.L., F.M., and R.R.P. held official licenses for trapping wildlife in Spain. Capture permits were provided by the Dirección General del Medio Natural, Junta de Castilla y León. This study was supported by projects ECOVOLE (grant CGL2012-35348), ECOTULA (grant CGL2015-66962-C2-1-R), and RESERTULA (grant CLG2015-66962-C2-2-R), which were funded by the Ministerio de Economía y Competitividad MINECO/FEDER, Spain. R.R.P. was supported by a PhD studentship from the University of Valladolid (co-funded by Banco Santander).Peer reviewedPublisher PD

MPI+OpenMP tasking scalability for the simulation of the human brain

The simulation of the behavior of the Human Brain is one of the most ambitious challenges today with a non-end of important applications. We can find many different initiatives in the USA, Europe and Japan which attempt to achieve such a challenging target. In this work we focus on the most important European initiative (Human Brain Project) and on one of the tools (Arbor). This tool simulates the spikes triggered in a neuronal network by computing the voltage capacitance on the neurons' morphology, being one of the most precise simulators today. In the present work, we have evaluated the use of MPI+OpenMP tasking on top of the Arbor simulator. In this paper, we present the main characteristics of the Arbor tool and how these can be efficiently managed by using MPI+OpenMP tasking. We prove that this approach is able to achieve a good scaling even when computing a relatively low workload (number of neurons) per node using up to 32 nodes. Our target consists of achieving not only a highly scalable implementation based on MPI, but also to develop a tool with a high degree of abstraction without losing control and performance by using MPI+OpenMP tasking.We would like to apreciate the valuable feedback and help provided by Benjamin Cumming and Alexander Peyser. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1 and HBP SGA2), from the Spanish Ministry of Economy and Competitiveness under the project Computacion de Altas Prestaciones VII (TIN2015- ´ 65316-P) and the Departament d’Innovacio, Universitats i ´ Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programacio i Entorns d’Execuci ´ o Paral ´ ·lels (2014-SGR-1051). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grand agreement No.749516Peer ReviewedPostprint (author version