  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.
