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Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors.
Michael L Hines1, Hubert Eichner, Felix Schürmann
1Computer Science, Yale University, New Haven, CT, USA. michael.hines@yale.edu
Journal of Computational Neuroscience
|January 25, 2008
Summary
Splitting neuron tree topology equations across processors enhances neural network simulations. This method achieves load balance and near-ideal runtime scaling, even with varying cell sizes.
Area of Science:
- Computational neuroscience
- Parallel computing
Background:
- Neural network simulations require significant computational resources.
- Load balancing is crucial for efficient parallel processing in simulations.
Purpose of the Study:
- To introduce and evaluate a cell splitting method for improving load balance in neural network simulations.
- To assess the impact of cell splitting on simulation accuracy, stability, and runtime.
Main Methods:
- Neuron tree topology equations were split into two subtrees.
- Subtrees were solved independently on different processors.
- Communication costs were minimized to two double precision values per subtree per time step.
Main Results:
- Cell splitting achieved load balance, particularly with diverse cell sizes and processor counts.
- No change in accuracy, stability, or computational effort was observed.
- The method demonstrated good runtime scaling on parallel processors.
Conclusions:
- Cell splitting is an effective technique for load balancing in neural network simulations.
- This approach enables efficient use of parallel processors, improving simulation performance.
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