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Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring.

Jari Pronold1,2, Jakob Jordan3, Brian J N Wylie4

  • 1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany.

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A new algorithm for spiking neuronal network simulations optimizes spike delivery, reducing simulation time by up to 40%. This parallelizable approach enhances efficiency in large-scale neural modeling.

Keywords:
distributed computingirregular access patternlarge-scale simulationmemory-access bottleneckparallel computingsparsityspiking neural networks

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Area of Science:

  • Computational Neuroscience
  • High-Performance Computing
  • Artificial Neural Networks

Background:

  • Spiking neuronal network (SNN) simulations are computationally intensive, with significant time spent on spike delivery.
  • Current SNN simulation codes face performance bottlenecks in efficiently routing irregular, unsorted spikes to target neurons across distributed compute nodes.
  • The complexity of spike delivery increases with network size, demanding optimized algorithms for large-scale neural modeling.

Purpose of the Study:

  • To analyze the emergence of sparsity in spike delivery for large-scale SNNs.
  • To investigate algorithmic improvements for efficient spike delivery in production SNN simulation code.
  • To reduce the computational overhead associated with routing spikes to target neurons and synapse types.

Main Methods:

  • Analytical derivation of spike sparsity emergence across a range of network sizes (100,000 to 1 billion neurons).
  • Profiling of existing SNN simulation code to identify performance bottlenecks in spike delivery.
  • Development and implementation of a new parallel sorting algorithm for efficient spike dispatching and delivery.

Main Results:

  • The new algorithm divides spikes equally among threads and sorts them in parallel by target thread and synapse type.
  • Each thread processes only its assigned section of spikes, reducing the total number of times spikes are examined to two.
  • The optimized spike delivery mechanism halves the instruction count, leading to simulation time reductions of up to 40%.

Conclusions:

  • Spike delivery in SNN simulations is a fully parallelizable process suitable for many-core systems.
  • Algorithmic improvements in spike delivery can significantly accelerate large-scale neural network simulations.
  • Future advancements require reducing memory access latency, paving the way for techniques like software pipelining and prefetching.