Multi-GPU SNN Simulation with Static Load Balancing
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Abstract (arXiv)
We present a SNN simulator which scales to millions of neurons, billions of synapses, and 8 GPUs. This is made possible by 1) a novel, cache-aware spike transmission algorithm 2) a model parallel multi-GPU distribution scheme and 3) a static, yet very effective load balancing strategy. The simulator further features an easy to use API and the ability to create custom models. We compare the proposed simulator against two state of the art ones on a series of benchmarks using three well-established models. We find that our simulator is faster, consumes less memory, and scales linearly with the number of GPUs.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 51 references that are in the map. First posted on arXiv 9 Feb 2021.
Builds on
6 of 51 references.
References · 6
- 2018BindsNET: A machine learning-oriented spiking neural networks library in Pythoncited by 42
- 2019SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuroncited by 23
- 2016BrainFrame: A node-level heterogeneous accelerator platform for neuron simulationscited by 2
- 2019Faster and Simpler SNN Simulation with Work Queuescited by 2
- 2020Fast simulations of highly-connected spiking cortical models using GPUscited by 2
- 2021Library-based Fast Algorithm for Simulating the Hodgkin-Huxley Neuronal Networkscited by 2
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Citing papers per year: 2021: 1.
1 paper.