OneGraph: A Cross-Architecture Framework for Large-Scale Graph Computing on GPUs Based on oneAPI
Graph computing is widely used in real scenarios such as transportation, social networking, and biological information network analysis, and GPU is a commonly used graph computing acceleration tool. However, the size of data sets in the real world often exceeds the GPU memory load, and data transfer between GPU and CPU is frequent, resulting in a large amount of GPU time in busy waiting, which seriously affects program performance. We found that during graph computing, applications always sequentially scan graph data within a large memory access range. Based on this finding, we divided GPU memory to store reusable data and other data separately to optimize the memory access behavior of the program. Based on this, we propose a general and easy-to-extend graph computing framework.
We used Intel oneAPI as a programming tool to implement the framework’s prototype OneGraph.
Speaker(s)
Shiyang Li - Nankai University/Students
Shiyang Li is a postgraduate candidate in Nankai University’s Embedded System and Information Security Research Office, and his main research direction is high-performance computing and operating system.
Yuting Peng - Nankai University/Students
Yuting Peng is an undergraduate student majoring in computer science and technology. She has participated in high-performance computing and computer vision-related work, and her current research interest is computer vision.
