In this section, the performance of the optimized implementation (using Modin-Dask and oneDAL) is compared against the unoptimized version (using native panda and XGBoost  libraries), with a performance breakdown by the three stages in the pipeline. The comparison is performed for both the New York City (NYC) Taxi dataset and the Higgs dataset. The NYC dataset is used to study the performance of the regression function; the Higgs dataset is used to study the classification problem.
The comparison results for the NYC dataset are shown in Figure 5. The overall speedup of the optimized pipeline is ~3x, with the following breakdown:
Shown in Figure 6 is the performance of the ML pipeline using the Higgs dataset. Again, the optimized implementation (using Modin-Dask and oneDAL) is compared against the native panda and XGBoost libraries, and the performance is also broken down by the three stages in the ML pipeline. Note that only 20 computing cores were used for processing the Higgs dataset. Because the Higgs dataset is relatively small (7.5 GB vs 64GB for the NYC dataset), it was not large enough to be effectively partitioned beyond 20 cores. The results show a speedup of ~3x for the ETL stage and a speedup of ~2.25x for the Inference stage. Again, the XGBoost model training is converted to oneDAL using daal4py, and because the Higgs dataset size is smaller than the NYC dataset, the overhead becomes more of a factor, that is, the Training stage for the optimized version is taking more time than native pandas and XGBoost in the Training stage. As a result, the overall speedup of the optimized pipeline is ~1.9x.