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.

中文翻译：

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知识图嵌入方法的运行时性能基准

本文希望重点介绍 KGE 算法的最先进实现的运行时性能，在内存占用和执行时间方面。尽管人们对 KGE 方法的兴趣迅速增长，但迄今为止很少有人关注它们的比较和评估。特别是，以前的工作主要集中在特定任务（例如链接预测）的准确性方面的性能。在这方面，提出了一个框架，用于针对具有不同属性的图评估可用的 KGE 实现，特别关注所采用的优化策略的有效性。图和模型已经利用不同的体系结构进行了训练，以启发模型和它们所训练的体系结构的特征和属性。本文档中的一些实验结果表明，多线程是有效的，但在 CPU 的情况下，随着线程数量的增加，收益会减少。GPU 被证明是给定任务的最佳架构，即使带有一些矢量化指令的 CPU 仍然表现良好。最后，用于加载图的 RAM 利用率在不同架构之间永远不会改变，并且仅取决于图的类型，而不取决于模型。