# Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
## 3 Results
### Cortical Microcircuit Model
Both NEST GPU (_onboard_) and GeNN (without the _building_ phase) achieve fast network construction times of less than a second. The fastest overall network construction takes 0.499(0.10) s, as measured with NEST GPU (_onboard_) using DC input on the A100 GPU, the data center GPU of the latest architecture tested. The time measured using the RTX 4090 is also compatible with the A100 result; the measured times with the V100 and the consumer GPU RTX 2080 Ti are also close. Tables 2-4 provide the measured values for reference. Figure 4: Performance comparison of the network construction phase for different simulators and hardware configurations on the cortical microcircuit model. Data for NEST GPU (_onboard_) are the same as in Figure 3. (**A**) Network construction time of the model in linear scale for different simulators and hardware configurations. (**B**) as in (**A**) but with logarithmic y-axis scale. In both panels, the _building_ phase of GeNN is placed on top of the bar, breaking with the otherwise chronological order because this phase is not always required, and, at the same time, this display makes the shorter _loading_ phase visible in the plot with the logarithmic y-axis. Error bars show the standard deviation of the overall network construction phase over ten simulations using different random seeds. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**NeST GPU (onboard)**} & \multicolumn{6}{c}{**NEST GPU (offboard)**} & \multicolumn{2}{c}{**NEST 3.3 (CPU)**} \\ \cline{2-7}  & **V100** & **A100** & **2080 Ti** & **4090** & **V100** & **A100** & **2080 Ti** & **4090** & \(\mathbf{2\times 64}\) **Cores** \\ \hline Initialization & \(5.04(0.13)\) & \(1.44(0.08)\) & \(1.75(0.16)\) & \(1.97(0.09)\) & \(5.1(0.4)\) & \(1.5(0.4)\) & \(1.62(0.04)\) & \(1.86(0.04)\) & \(0.018\) \\  & \(\times 10^{-4}\) & \(\times 10^{-3}\) & \(\times 10^{-4}\) & \(\times 10^{-4}\) & \(\times 10^{-4}\) & \(\times 10^{-3}\) & \(\times 10^{-4}\) & \(\times 10^{-4}\) & \((0.003)\) \\ \hline Node creation & \(7.0(0.5)\) & \(6.6(0.3)\) & \(1.43(0.13)\) & \(1.64(0.04)\) & \(3.01(0.02)\) & \(3.28(0.03)\) & \(1.91(0.02)\) & \(1.79(0.03)\) & \(0.392\) \\  & \(\times 10^{-3}\) & \(\times 10^{-3}\) & \(\times 10^{-3}\) & \(\times 10^{-3}\) & \(3.01(0.02)\) & \(3.28(0.03)\) & \(1.91(0.02)\) & \(1.79(0.03)\) & \(0.392\) \\ \hline Node connection & \(0.1028\) & \(0.0790\) & \(0.31(0.02)\) & \(0.1538\) & \(54.65(0.17)\) & \(55.89(0.19)\) & \(40.8(0.5)\) & \(44.2(0.7)\) & \(1.53(0.07)\) \\ \hline Calibration & \(0.5785\) & \(0.412\) & \(0.6011\) & \(0.3632\) & \(1.993\) & \(2.059\) & \(2.194\) & \(2.181\) & \(2.352\) \\  & \((0.0013)\) & \((0.008)\) & \((0.0006)\) & \((0.0003)\) & \((0.012)\) & \((0.016)\) & \((0.015)\) & \((0.015)\) \\ \hline
**Network construction** & \(0.6888\) & \(\mathbf{0.499(0.10)}\) & \(0.91(0.02)\) & \(\mathbf{0.5189}\) & \(\mathbf{0.5005}\) & \(59.65(0.19)\) & \(61.23(0.19)\) & \(44.9(0.5)\) & \(48.1(0.7)\) & \(4.30(0.07)\) \\ \hline Simulation & \(6.36(0.02)\) & \(7.32(0.05)\) & \(5.61(0.03)\) & \(3.86(0.01)\) & \(6.530\) & \(7.43(0.02)\) & \(5.604\) & \(3.953\) \\  & \(7.77(0.15)\) & \(0.018\) & \(0.366(0.01)\) & \((0.012)\) & \(7.43(0.02)\) & \((0.016)\) & \((0.013)\) & \(7.77(0.15)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of GeNN. All times are in seconds with notation (mean (standard deviation)). Simulation time is calculated for a simulation of 10 s of biological time.