Shanghai Jiao Tong University

Pengyu Wang

Jing Wang

Chao Li

Minyi Guo

Haojin Zhu

Jinyang Guo

Chuanming Shao

An adaptive, large graph-oriented unified memory management method is proposed; according to the order of priorities of different types of graph data structure in a graph computing application, whether the current GPU memory is full is sequentially determined by means of GPU memory checking; whether the size of current graph data exceeds the available memory capacity of the GPU is determined by means of data overflow checking; and then a unified memory management policy is configured; the method uses different graph algorithms based on the characteristics of graph data structure and the size of the available GPU memory; the method can significantly improve the performance of processing large graphs, which exceed the video memory capacity under a unified memory architecture, including improving GPU bandwidth utilization, reducing the number of page faults and the overhead of processing page faults, and speeding up the execution of graph computing programs.

Adaptive unified memory management method and system for large-scale graphs

A graph sampling and random walk acceleration method and system based on GPU. The graph data is read from the storage medium through the CPU and converted into CSR format and then output to the GPU. The GPU works based on the defined working mode: Real-time generation of alias table and sampling; or offline judgment whether there is a pre-generated alias table and sampling, which executes the alias method efficiently and in parallel, and can significantly improve the performance of graph data processing on the same hardware platform, including improving the sampling throughput and reducing the overall running time.

Graph sampling and random walk acceleration method and system on GPU

Growing community of inventors

Shanghai, China

Pengyu Wang