The human brain consists of a large number of interconnected neurons communicating via exchange of electrical spikes. Simulations play an important role in better understanding electrical activity in the brain and offers a way to to compare measured data to simulated data such that experimental data can be interpreted better. A key component in such simulations is an efficient solver for the Hines matrices used in computing inter-neuron signal propagation. In order to achieve high performance simulations, it is crucial to have an efficient solver algorithm. In this report we explain a new parallel GPU solver for these matrices which offers fine grained parallelization and allows for work balancing during the simulation setup.
Cite
@article{arxiv.1810.12742,
title = {Efficient Tree Solver for Hines Matrices on the GPU},
author = {Felix Huber},
journal= {arXiv preprint arXiv:1810.12742},
year = {2018}
}