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cuHinesBatch: solving multiple hines systems on GPUs Human Brain Project

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10.1016/j.procs.2017.05.145
 
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hdl:2117/106615

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Valero-Lara, Pedro
Martinez-Perez, Ivan
Peña, Antonio J.
Martorell Bofill, XavierMés informacióMés informacióMés informació
Sirvent, Raul
Labarta Mancho, Jesús JoséMés informacióMés informacióMés informació
Document typeArticle
Defense date2017
PublisherElsevier
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
ProjectHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (EC-H2020-720270)
COMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
IJCI-2015-23266 (MINECO-IJCI-2015-23266)
Abstract
The simulation of the behavior of the Human Brain is one of the most important challenges today in computing. 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. 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 NVIDIA 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. To evaluate the impact of the optimizations on real inputs, we have used 6 different morphologies in terms of size and branches. Our studies have proven that the optimizations proposed in the present work can achieve a 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 K80 NVIDIA GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling even when dealing with number of neurons.
CitationValero-Lara, P., Martinez-Perez, I., Peña, A., Martorell, X., Sirvent, R., Labarta, J. cuHinesBatch: solving multiple hines systems on GPUs Human Brain Project. "Procedia computer science", 2017, vol. 108, p. 566-575. 
URIhttp://hdl.handle.net/2117/106615
DOI10.1016/j.procs.2017.05.145
ISSN1877-0509
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S1877050917307172#fn1
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  • Computer Sciences - Articles de revista [293]
  • Departament d'Arquitectura de Computadors - Articles de revista [1.003]
  • CAP - Grup de Computació d'Altes Prestacions - Articles de revista [380]
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