Abstract
Neural tissue simulation extends requirements and constraints of previous neuronal and neural circuit simulation methods, creating a tissue coordinate system. We have developed a novel tissue volume decomposition, and a hybrid branched cable equation solver. The decomposition divides the simulation into regular tissue blocks and distributes them on a parallel multithreaded machine. The solver computes neurons that have been divided arbitrarily across blocks. We demonstrate thread, strong, and weak scaling of our approach on a machine with more than 4000 nodes and up to four threads per node. Scaling synapses to physiological numbers had little effect on performance, since our decomposition approach generates synapses that are almost always computed locally. The largest simulation included in our scaling results comprised 1 million neurons, 1 billion compartments, and 10 billion conductance-based synapses and gap junctions. We discuss the implications of our ultrascalable Neural Tissue Simulator, and with our results estimate requirements for a simulation at the scale of a human brain.
Highlights
ObjectivesWe aimed to support models that require global communication
Our results indicate that both strong and weak scaling are properties of all three stages of initialization performed by the Neural Tissue Functor, with expected total initialization times of 1–2 min per neuron per processor across all simulation and machine sizes reported here with on average 10,000 synapses per neuron
Simulation Scaling Results Here we report the results of scaling the Neural Tissue Simulator using several methods in order to evaluate its performance over a variety of simulation and machine sizes and configurations
Summary
We aimed to support models that require global communication
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