Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster
Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2022.883333/full |
| _version_ | 1811325387390582784 |
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| author | Gianmarco Tiddia Gianmarco Tiddia Bruno Golosio Bruno Golosio Jasper Albers Jasper Albers Johanna Senk Francesco Simula Jari Pronold Jari Pronold Viviana Fanti Viviana Fanti Elena Pastorelli Pier Stanislao Paolucci Sacha J. van Albada Sacha J. van Albada |
| author_facet | Gianmarco Tiddia Gianmarco Tiddia Bruno Golosio Bruno Golosio Jasper Albers Jasper Albers Johanna Senk Francesco Simula Jari Pronold Jari Pronold Viviana Fanti Viviana Fanti Elena Pastorelli Pier Stanislao Paolucci Sacha J. van Albada Sacha J. van Albada |
| author_sort | Gianmarco Tiddia |
| collection | DOAJ |
| description | Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST. |
| first_indexed | 2024-04-13T14:32:16Z |
| format | Article |
| id | doaj.art-f290692a20e2448bbd7f2fc006908d81 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-04-13T14:32:16Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-f290692a20e2448bbd7f2fc006908d812022-12-22T02:43:10ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962022-07-011610.3389/fninf.2022.883333883333Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU ClusterGianmarco Tiddia0Gianmarco Tiddia1Bruno Golosio2Bruno Golosio3Jasper Albers4Jasper Albers5Johanna Senk6Francesco Simula7Jari Pronold8Jari Pronold9Viviana Fanti10Viviana Fanti11Elena Pastorelli12Pier Stanislao Paolucci13Sacha J. van Albada14Sacha J. van Albada15Department of Physics, University of Cagliari, Monserrato, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, ItalyDepartment of Physics, University of Cagliari, Monserrato, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, ItalyInstitute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, GermanyRWTH Aachen University, Aachen, GermanyInstitute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, GermanyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, ItalyInstitute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, GermanyRWTH Aachen University, Aachen, GermanyDepartment of Physics, University of Cagliari, Monserrato, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, ItalyInstitute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, GermanyFaculty of Mathematics and Natural Sciences, Institute of Zoology, University of Cologne, Cologne, GermanySpiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST.https://www.frontiersin.org/articles/10.3389/fninf.2022.883333/fullcomputational neurosciencespiking neural networkssimulationsGPU (CUDA)primate cortexmulti-area model of cerebral cortex |
| spellingShingle | Gianmarco Tiddia Gianmarco Tiddia Bruno Golosio Bruno Golosio Jasper Albers Jasper Albers Johanna Senk Francesco Simula Jari Pronold Jari Pronold Viviana Fanti Viviana Fanti Elena Pastorelli Pier Stanislao Paolucci Sacha J. van Albada Sacha J. van Albada Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster Frontiers in Neuroinformatics computational neuroscience spiking neural networks simulations GPU (CUDA) primate cortex multi-area model of cerebral cortex |
| title | Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster |
| title_full | Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster |
| title_fullStr | Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster |
| title_full_unstemmed | Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster |
| title_short | Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster |
| title_sort | fast simulation of a multi area spiking network model of macaque cortex on an mpi gpu cluster |
| topic | computational neuroscience spiking neural networks simulations GPU (CUDA) primate cortex multi-area model of cerebral cortex |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2022.883333/full |
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