Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parall...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9598 |
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| author | Bruno Golosio Jose Villamar Gianmarco Tiddia Elena Pastorelli Jonas Stapmanns Viviana Fanti Pier Stanislao Paolucci Abigail Morrison Johanna Senk |
| author_facet | Bruno Golosio Jose Villamar Gianmarco Tiddia Elena Pastorelli Jonas Stapmanns Viviana Fanti Pier Stanislao Paolucci Abigail Morrison Johanna Senk |
| author_sort | Bruno Golosio |
| collection | DOAJ |
| description | Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling. |
| first_indexed | 2024-03-10T23:28:38Z |
| format | Article |
| id | doaj.art-65b7e6fbae6a4708b6c1573bcb92551f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:28:38Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-65b7e6fbae6a4708b6c1573bcb92551f2023-11-19T07:49:15ZengMDPI AGApplied Sciences2076-34172023-08-011317959810.3390/app13179598Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU DevicesBruno Golosio0Jose Villamar1Gianmarco Tiddia2Elena Pastorelli3Jonas Stapmanns4Viviana Fanti5Pier Stanislao Paolucci6Abigail Morrison7Johanna Senk8Department of Physics, University of Cagliari, 09042 Monserrato, ItalyInstitute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, GermanyDepartment of Physics, University of Cagliari, 09042 Monserrato, ItalyIstituto Nazionale di Fisica Nucleare, Sezione di Roma, 00185 Roma, ItalyInstitute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, GermanyDepartment of Physics, University of Cagliari, 09042 Monserrato, ItalyIstituto Nazionale di Fisica Nucleare, Sezione di Roma, 00185 Roma, ItalyInstitute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, GermanyInstitute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, GermanySimulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling.https://www.mdpi.com/2076-3417/13/17/9598spiking neuronal networksGPUcomputational neurosciencenetwork connectivity |
| spellingShingle | Bruno Golosio Jose Villamar Gianmarco Tiddia Elena Pastorelli Jonas Stapmanns Viviana Fanti Pier Stanislao Paolucci Abigail Morrison Johanna Senk Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices Applied Sciences spiking neuronal networks GPU computational neuroscience network connectivity |
| title | Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices |
| title_full | Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices |
| title_fullStr | Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices |
| title_full_unstemmed | Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices |
| title_short | Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices |
| title_sort | runtime construction of large scale spiking neuronal network models on gpu devices |
| topic | spiking neuronal networks GPU computational neuroscience network connectivity |
| url | https://www.mdpi.com/2076-3417/13/17/9598 |
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