Progress Summary

Our project's model is currently trained on models where the CPU is the main consumer of dynamic power. Results are fragmented between the two test machines, lolcat and rickroll, due to data collection errors. The next steps planned are finishing data collection, examining GPU benchmarks possibly with the added benefit of instrumenting the GPU, and analyzing oddness within the results.

For FDTD3D (GPU benchmark) rickroll’s MSE, rMSE/mean and DRE display a delta in the results. The MSE at frequency 2000 is 4.13 and goes to 318.29 at frequency 2200.  DRE repeats this delta at the two frequencies moving from 0.10 to 0.63.  Root MSE/Mean’s 2000 to 2200 frequency delta is a change from 0.01 to 0.12. A reasonable explanation would be to hypothesize that before 2200 the CPU is bound. Other data presently does not support this explanation.

Using two benchmarks, nbody and binomialOptions, as sets of train and test  (same model both train and test, using nbody as train but binomialOption as test and vise versa) lolcat’s results stress how unaware the model is of the GPU’s influence on the expected power (but the power does correlate well to CPU & disk for this workload). The model cannot predict a reasonable expected power when the GPU is stressed in addition to the CPU, or the GPU is stressed but not the CPU.

Once calibration data recollection on locat finishes and is analyzed for errors, the next step will be proceeding on GPU awareness. For more insight on the GPU’s role with power consumption NVIDIA-smi will be implemented for GPU instrumentation.  The model can’t predict beyond the CPU exercising at 100% but if a GPU aware component is added the prediction should be less erroneous.