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Heuristic Tree-Partition-Based Parallel Method for Biophysically Detailed Neuron Simulation.

Bibliographic Details
Title: Heuristic Tree-Partition-Based Parallel Method for Biophysically Detailed Neuron Simulation.
Authors: Zhang, Yichen, Du, Kai, Huang, Tiejun
Source: Neural Computation; Apr2023, Vol. 35 Issue 4, p627-644, 18p, 3 Diagrams, 2 Charts, 3 Graphs
Subject Terms: Parallel algorithms, Neurons, Heuristic algorithms, Linear equations, Process capability, Computational neuroscience
Abstract: Biophysically detailed neuron simulation is a powerful tool to explore the mechanisms behind biological experiments and bridge the gap between various scales in neuroscience research. However, the extremely high computational complexity of detailed neuron simulation restricts the modeling and exploration of detailed network models. The bottleneck is solving the system of linear equations. To accelerate detailed simulation, we propose a heuristic tree-partition-based parallel method (HTP) to parallelize the computation of the Hines algorithm, the kernel for solving linear equations, and leverage the strong parallel capability of the graphic processing unit (GPU) to achieve further speedup. We formulate the problem of how to get a fine parallel process as a tree-partition problem. Next, we present a heuristic partition algorithm to obtain an effective partition to efficiently parallelize the equation-solving process in detailed simulation. With further optimization on GPU, our HTP method achieves 2.2 to 8.5 folds speedup compared to the state-of-the-art GPU method and 36 to 660 folds speedup compared to the typical Hines algorithm. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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PubType: Academic Journal
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  Data: Heuristic Tree-Partition-Based Parallel Method for Biophysically Detailed Neuron Simulation.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yichen%22">Zhang, Yichen</searchLink><br /><searchLink fieldCode="AR" term="%22Du%2C+Kai%22">Du, Kai</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Tiejun%22">Huang, Tiejun</searchLink>
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  Data: Neural Computation; Apr2023, Vol. 35 Issue 4, p627-644, 18p, 3 Diagrams, 2 Charts, 3 Graphs
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  Data: <searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Neurons%22">Neurons</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+equations%22">Linear equations</searchLink><br /><searchLink fieldCode="DE" term="%22Process+capability%22">Process capability</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+neuroscience%22">Computational neuroscience</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Biophysically detailed neuron simulation is a powerful tool to explore the mechanisms behind biological experiments and bridge the gap between various scales in neuroscience research. However, the extremely high computational complexity of detailed neuron simulation restricts the modeling and exploration of detailed network models. The bottleneck is solving the system of linear equations. To accelerate detailed simulation, we propose a heuristic tree-partition-based parallel method (HTP) to parallelize the computation of the Hines algorithm, the kernel for solving linear equations, and leverage the strong parallel capability of the graphic processing unit (GPU) to achieve further speedup. We formulate the problem of how to get a fine parallel process as a tree-partition problem. Next, we present a heuristic partition algorithm to obtain an effective partition to efficiently parallelize the equation-solving process in detailed simulation. With further optimization on GPU, our HTP method achieves 2.2 to 8.5 folds speedup compared to the state-of-the-art GPU method and 36 to 660 folds speedup compared to the typical Hines algorithm. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1162/neco_a_01565
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
        StartPage: 627
    Subjects:
      – SubjectFull: Parallel algorithms
        Type: general
      – SubjectFull: Neurons
        Type: general
      – SubjectFull: Heuristic algorithms
        Type: general
      – SubjectFull: Linear equations
        Type: general
      – SubjectFull: Process capability
        Type: general
      – SubjectFull: Computational neuroscience
        Type: general
    Titles:
      – TitleFull: Heuristic Tree-Partition-Based Parallel Method for Biophysically Detailed Neuron Simulation.
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            NameFull: Zhang, Yichen
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            NameFull: Du, Kai
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            NameFull: Huang, Tiejun
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            – D: 01
              M: 04
              Text: Apr2023
              Type: published
              Y: 2023
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              Value: 35
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            – TitleFull: Neural Computation
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