  This paper wants to focus on providing a characterization of the runtime
performances of state-of-the-art implementations of KGE alghoritms, in terms of
memory footprint and execution time. Despite the rapidly growing interest in
KGE methods, so far little attention has been devoted to their comparison and
evaluation; in particular, previous work mainly focused on performance in terms
of accuracy in specific tasks, such as link prediction. To this extent, a
framework is proposed for evaluating available KGE implementations against
graphs with different properties, with a particular focus on the effectiveness
of the adopted optimization strategies. Graphs and models have been trained
leveraging different architectures, in order to enlighten features and
properties of both models and the architectures they have been trained on. Some
results enlightened with experiments in this document are the fact that
multithreading is efficient, but benefit deacreases as the number of threads
grows in case of CPU. GPU proves to be the best architecture for the given
task, even if CPU with some vectorized instructions still behaves well.
Finally, RAM utilization for the loading of the graph never changes between
different architectures and depends only on the type of graph, not on the
model.
