Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring

Abstract

Generic simulation code for spiking neuronal networks spends the major part of time in the phase where spikes have arrived at a compute node and need to be delivered to their target neurons. These spikes were emitted over the last interval between communication steps by source neurons distributed across many compute nodes and are inherently irregular with respect to their targets. For finding the targets, the spikes need to be dispatched to a three-dimensional data structure with decisions on target thread and synapse type to be made on the way. With growing network size a compute node receives spikes from an increasing number of different source neurons until in the limit each synapse on the compute node has a unique source. Here we show analytically how this sparsity emerges over the practically relevant range of network sizes from a hundred thousand to a billion neurons. By profiling a production code we investigate opportunities for algorithmic changes to avoid indirections and branching. Every thread hosts an equal share of the neurons on a compute node. In the original algorithm all threads search through all spikes to pick out the relevant ones. With increasing network size the fraction of hits remains invariant but the absolute number of rejections grows. An alternative algorithm equally divides the spikes among the threads and sorts them in parallel according to target thread and synapse type. After this every thread completes delivery solely of the section of spikes for its own neurons. The new algorithm halves the number of instructions in spike delivery which leads to a reduction of simulation time of up to 40 %. Thus, spike delivery is a fully parallelizable process with a single synchronization point and thereby well suited for many-core systems. Our analysis indicates that further progress requires a reduction of the latency instructions experience in accessing memory.

References in corpus (4)

• The scientific case for brain simulations
• Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures
• Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring
• Event-based update of synapses in voltage-based learning rules

Cited by in corpus (5)

• Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers
• Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring
• A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
• Metadata practices for simulation workflows
• Exploiting network topology in brain-scale simulations of spiking neural networks