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.

中文翻译：

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具有完美静态负载平衡的多GPU SNN仿真

我们提供了一个SNN模拟器，可扩展到数百万个神经元，数十亿个突触和8个GPU。通过1）一种新颖的，可识别缓存的尖峰传输算法，2）一种模型并行多GPU分配方案以及3）一种静态但非常有效的负载平衡策略，使之成为可能。该模拟器还具有易于使用的API和创建自定义模型的功能。我们使用三个公认的模型，在一系列基准测试中将拟议的模拟器与两个最新的模拟器进行了比较。我们发现我们的模拟器速度更快，消耗的内存更少，并且与GPU的数量成线性比例。