论文标题
知识图嵌入方法的运行时性能基准
Runtime Performances Benchmark for Knowledge Graph Embedding Methods
论文作者
论文摘要
本文希望专注于提供KGE Alghoritms最新实现的运行时性能，从内存足迹和执行时间方面。尽管对KGE方法的兴趣迅速增长，但到目前为止，很少关注他们的比较和评估。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。在此范围内，提出了一个框架，以评估针对具有不同属性的图形的可用KGE实现，并特别关注采用优化策略的有效性。为了启发模型的功能和属性，已经对图形和模型进行了训练，以利用不同的体系结构。本文档中的实验启发了一些结果，这是多线程有效的事实，但是随着CPU的螺纹数量的增长而受益下降。 GPU被证明是给定任务的最佳体系结构，即使具有一些矢量化指令的CPU仍然表现得很好。最后，用于加载图的RAM利用率永远不会在不同的体系结构之间发生变化，仅取决于图的类型，而不是模型。
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.
