Computer Science > Neural and Evolutionary Computing
[Submitted on 9 Feb 2021 (v1), last revised 22 Apr 2021 (this version, v2)]
Title:Multi-GPU SNN Simulation with Static Load Balancing
View PDFAbstract: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.
Submission history
From: Dennis Bautembach [view email][v1] Tue, 9 Feb 2021 07:07:34 UTC (141 KB)
[v2] Thu, 22 Apr 2021 18:50:23 UTC (142 KB)
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