  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.
