FIG. 7.

Performance comparison of cubewalkers, cana, and booleannet on consumer hardware. 72 Cell Collective models were run using each tool using synchronous update. Timings were generated on a PC with an AMD Ryzen $53600\mathrm{X}$ CPU at $3.8\mathrm{G}\mathrm{H}\mathrm{z}$ and a 2560 CUDA-core 1605 MHz NVIDIA 2070S GPU. Default methods were run without additional parallelization. For the cubewalkers tests, 2500 time steps and 2500 walkers (initial conditions) were used; for cana, 500 time steps and 500 walkers were used; and for booleannet, 100 time steps and 100 initial conditions were used. Thus, for each network, cana computed $5\times$ as many time steps for $5\times$ as many initial conditions as booleannet for an overall disadvantage of $25\times$. Similarly, cubewalkers computed $5\times$ as many time steps for $5\times$ as many initial conditions as cana, for a $25\times$ disadvantage relative to cana and a $625\times$ disadvantage relative to booleannet. The raw time to complete these tasks is plotted in the left panel, where we observe that cubewalkers consistently finishes its tasks an order of magnitude faster than the other methods, despite the fact that it has been given significantly more computational work. In the right panel, the average computation time per network node per time step per initial condition in these trials is plotted; this corresponds to the average (amortized) time to evaluate and apply an update function to a node. Here, we see that these amortized evaluations occur on the order of nanoseconds for cubewalkers, while they occur on the order of microseconds for cana and hundreds of microseconds for booleannet.