High-Performance Computing for Graph AI: A Top-Down Perspective

A graph, made up of vertices and edges, is a natural representation for many real-world applications. Graph artificial intelligence (AI) techniques, especially graph neural networks (GNNs), are becoming increasingly important in modern machine learning and data analysis, as they can accurately represent high-dimensional features of vertices, edges, and structure information into low-dimensional embeddings. They have become a valuable area of study for students in fields like computer science, data science, and AI. However, the students are facing two challenges to grasp the knowledge of GNNs, including (i) learning GNNs often requires multidiscipline knowledge, and (ii) resources for learning GNNs are often fragmented.Motivated by that, we designed a self-contained course module on high-performance computing for graph AI: from a top-down perspective based on our study in this area for the past years. In particular, we divide them into four levels from the top to the bottom, including (i) level 1: graph theory basics, (ii) level 2: fundamental theories of GNNs, (iii) level 3: efficient graph AI computation framework, and (iv) level 4: GPU architecture and programming. In addition, we have disseminated part of this module into different educational activities, such as courses and tutorials.This paper is submitted for the Research to Education track of EduPar-25.