## 5 Experiments
### Results
In Figure 7, we compare the iteration delay distribution of the measured data and the analyzed results of the experiments. The influence of different data on the applications execution time is shown for the experiments _Sobel-TL1_ and _JPEG-TL1_ in Figure 7a,b. It can be clearly seen that the consideration of different data for the analysis is valuable. The different shape of the distribution as well as the lower average iteration duration were correctly predicted by the analysis. \begin{table}
\begin{tabular}{l r r r r r r r} \hline \hline
**Experiment** & **Measured** & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Gaussian**} & \multicolumn{2}{c}{**KDE**} \\ \hline _Sobel-CA1_ & 4593.26 & 4445.00 & \((-3.23\%,3.388)\) & 4444.84 & \((-3.23\%,0.436)\) & 4443.33 & \((-3.26\%,2.007)\) \\ _Sobel-TL1_ & 4593.26 & 4435.00 & \((-3.45\%,3.388)\) & 4434.84 & \((-3.45\%,0.404)\) & 4433.32 & \((-3.48\%,2.442)\) \\ _Sobel-ML1_ & 4593.26 & 4783.00 & \((4.13\%,3.388)\) & 4782.84 & \((4.13\%,0.428)\) & 4781.33 & \((4.09\%,2.291)\) \\ _Sobel-CA2_ & 2902.53 & 3100.00 & \((6.80\%,2.210)\) & 3092.60 & \((6.55\%,0.825)\) & 3091.98 & \((6.53\%,1.941)\) \\ _Sobel-TL2_ & 2902.53 & 2907.00 & \((0.14\%,3.134)\) & 2906.79 & \((0.15\%,0.947)\) & 2905.78 & \((0.11\%,1.607)\) \\ _Sobel-ML2_ & 2902.53 & 3039.00 & \((4.70\%,2.212)\) & 3038.79 & \((4.69\%,0.802)\) & 3037.78 & \((4.66\%,1.800)\) \\ _Sobel-CA4_ & 3097.43 & 4623.00 & \((49.25\%,2.665)\) & 4636.61 & \((49.69\%,2.528)\) & 4639.62 & \((49.79\%,4.891)\) \\ _Sobel-TL4_ & 3097.43 & 2939.99 & \((-5.08\%,2.564)\) & 2939.79 & \((-5.09\%,1.487)\) & 2938.78 & \((-5.12\%,1.330)\) \\ _Sobel-ML4_ & 3097.43 & 3105.00 & \((0.24\%,3.927)\) & 3104.80 & \((0.24\%,0.502)\) & 3103.78 & \((0.21\%,1.330)\) \\ \hline JPEG-CA1 & 2,385,860.12 & 2,384,114.00 & \((-0.07\%,1.877)\) & 2,384,156.45 & \((-0.07\%,0.354)\) & 2,384,088.70 & \((-0.07\%,0.061)\) \\ JPEG-TL1 & 2,385,860.12 & 2,384,094.00 & \((-0.07\%,1.877)\) & 2,384,136.45 & \((-0.07\%,0.354)\) & 2,384,068.00 & \((-0.08\%,0.061)\) \\ JPEG-ML1 & 2,385,860.12 & 2,389,605.00 & \((0.16\%,1.877)\) & 2,389,647.45 & \((0.16\%,0.445)\) & 2,389,579.70 & \((0.16\%,0.059)\) \\ JPEG-CA3 & 940,836.44 & 955,621.41 & \((1.57\%,2.422)\) & 955,688.20 & \((0.12\%,0.115)\) & 955,623.81 & \((1.57\%,0.071)\) \\ JPEG-TL3 & 940,836.44 & 941,122.00 & \((0.03\%,2.422)\) & 941,190.64 & \((0.04\%,0.185)\) & 941,115.53 & \((0.03\%,0.161)\) \\ JPEG-ML3 & 940,836.44 & 941,004.00 & \((0.02\%,2.422)\) & 941,068.01 & \((0.02\%,0.185)\) & 940,992.80 & \((0. Figure 7: Comparison of the observed distribution of execution times with the simulated ones using different computation models. In the lower part of Figure 7, we compare the distribution of the iteration delays of our ML model with data dependency (in blue) to the measured data (in green) of the Sobel and JPEG experiments. This comparison shows the efficiency of the computation time representation approaches (i.e., the data dependency, the KDE techniques) to the iteration delay. The distributions of simulated results show a similar shape between simulation and measured data. This means that the execution paths were well characterized and applied in our ML model. In Table 3, we compare the duration to measure 1,000,000 iterations of an application running on the real hardware platform with simulating the same application with the same mapping using different communication models. The table only shows the simulation runs with using the KDE based computation model. Since the inference technique is done before the simulation starts it only has a small impact on the overall simulation time. The KDE approach is the most compute intensive one. The simulation time does not include the time it takes to characterize the actors and communication. The factors within the parentheses show the speed-up of the simulation compared to the measurement. The _Speed-up_ column shows the speed-up of the ML communication model compared to the TL model.