dc.creator | Aboy, Blaine Corwyn D. | es |
dc.creator | Bariring, Edward James A. | es |
dc.creator | Carandang, Jym Paul | es |
dc.creator | Cabarle, Francis George C. | es |
dc.creator | Cruz, Ren Tristan de la | es |
dc.creator | Adorna, Henry N. | es |
dc.creator | Martínez del Amor, Miguel Ángel | es |
dc.date.accessioned | 2021-03-23T08:41:54Z | |
dc.date.available | 2021-03-23T08:41:54Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Aboy, B.C.D., Bariring, E.J.A., Carandang, J.P., Cabarle, F.G.C., Cruz, R.T.d.l., Adorna, H.N. y Martínez del Amor, M.Á. (2019). Optimizations in CuSNP Simulator for Spiking Neural P Systems on CUDA GPUs. En HPCS 2019: International Conference on High Performance Computing and Simulation (535-542), Dublin, Ireland: IEEE Computer Society. | |
dc.identifier.isbn | 978-1-7281-4484-9 | es |
dc.identifier.uri | https://hdl.handle.net/11441/106472 | |
dc.description.abstract | Spiking Neural P systems (in short, SNP systems)
are computing models based on living neurons. SNP systems are
non-deterministic and parallel, hence making use of a parallel
processor such as a graphics processing unit (in short, GPU)
is a natural candidate for simulations. Matrix representations
and algorithms were previously developed for simulating SNP
systems. In this work, our two results extend previous works in
simulating SNP systems in the GPU: (a) the number of neurons
the simulator can handle is now arbitrary; (b) SNP systems
are now represented in a dense instead of sparse way. The
impact in terms of time and space of these extensions to the
GPU simulator are analysed. As expected, SNP systems with
more neurons need more simulation time, although the simulator
performance can scale (i.e. perform better) with larger GPUs. The
dense representation helps in the simulation of larger systems. | es |
dc.description.sponsorship | Ministerio de Economía, Industria y Competitividad TIN2017-89842-P (MABICAP) | es |
dc.format | application/pdf | es |
dc.format.extent | 8 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | HPCS 2019: International Conference on High Performance Computing and Simulation (2019), pp. 535-542. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Membrane Computing | es |
dc.subject | Spiking neural P Systems | es |
dc.subject | GPU Computing | es |
dc.subject | CUDA | es |
dc.subject | Sparse Matrix-Vector | es |
dc.title | Optimizations in CuSNP Simulator for Spiking Neural P Systems on CUDA GPUs | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2017- 89842-P (MABICAP) | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9188174 | es |
dc.identifier.doi | 10.1109/HPCS48598.2019.9188174 | es |
dc.publication.initialPage | 535 | es |
dc.publication.endPage | 542 | es |
dc.eventtitle | HPCS 2019: International Conference on High Performance Computing and Simulation | es |
dc.eventinstitution | Dublin, Ireland | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Ministerio de Economia, Industria y Competitividad (MINECO). España | es |