大规模并行图形绘制与表示学习
Massively Parallel Graph Drawing and Representation Learning
摘要：
为了充分利用现代多核处理器的性能潜力，必须以多种方式并行化大数据的机器学习和数据采掘算法。今天的CPU由多个核心组成，每个核心都遵循一个独立的控制线程，每个核心都配备了多个算术单元，可以对多个数据对象的向量执行相同的操作。图嵌入，即将图的顶点转换为数值向量，是一项非常重要的数据采掘任务，对于图绘制（低维向量）和图表示学习（高维向量）非常有用。在本文中，我们提出了多核GEMPE（通过最小化预测熵的图形嵌入），一个信息理论的方法，可以生成低维和高维向量。MulticoreGEMPE应用MIMD（多指令多数据，使用OpenMP）和SIMD（单指令多数据，使用AVX-512）并行。我们提出了适用于其他基于图形的算法，如\emph{vectorized hashing}和\emph{vectorized reduction}的一般思想。我们的实验评估证明了我们的方法的优越性。
英文摘要：
To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways. Today's CPUs consist of multiple cores, each following an independent thread of control, and each equipped with multiple arithmetic units which can perform the same operation on a vector of multiple data objects. Graph embedding, i.e. converting the vertices of a graph into numerical vectors is a data mining task of high importance and is useful for graph drawing (low-dimensional vectors) and graph representation learning (high-dimensional vectors). In this paper, we propose MulticoreGEMPE (Graph Embedding by Minimizing the Predictive Entropy), an information-theoretic method which can generate low and high-dimensional vectors. MulticoreGEMPE applies MIMD (Multiple Instructions Multiple Data, using OpenMP) and SIMD (Single Instructions Multiple Data, using AVX-512) parallelism. We propose general ideas applicable in other graph-based algorithms like \emph{vectorized hashing} and \emph{vectorized reduction}. Our experimental evaluation demonstrates the superiority of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
