XGBoost vs LightGBM on a High Dimensional Dataset

I have recently completed a multi-class classification problem given as a take-home assignment for a data scientist position. It was a good opportunity to compare the two state-of-the-art implementations of gradient boosting decision trees which are XGBoost and LightGBM. Both algorithms are so powerful that they are prominent among the best performing machine learning models. The dataset contains over 60 thousand observations and 103 numerical features. The target variable contains 9 different classes.