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Efficient Tree Solver for Hines Matrices on the GPU (1810.12742v2)
Published 30 Oct 2018 in q-bio.QM and cs.NE
Abstract: 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.