Adaptive unified memory management method and system for large-scale graphs
AssigneeShanghai Jiao Tong University
InventorsChao Li, Pengyu Wang, Chuanming Shao, Jing Wang, Jinyang Guo, Haojin Zhu, Minyi Guo
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AssigneeShanghai Jiao Tong University
InventorsChao Li, Pengyu Wang, Chuanming Shao, Jing Wang, Jinyang Guo, Haojin Zhu, Minyi Guo
Estimating the term…
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.
An adaptive large graph-oriented unified memory management method, characterized in that, an ordered setting procedure according to a priority of different types of graph data in a graph computing application, and a policy making procedure based on whether a GPU memory is full and a relative size of targeted data and available GPU memory capacity, and a described unified memory management strategy configuration sets the management strategy of the current graph data by calling cudaMemPrefetchAsync and cudaMemAdvise instructions, and data usage hints include AccessedBy and ReadMostly, wherein the cudaMemPrefetchAsync moves part of data to the GPU memory, the cudaMemAdvise is set for a memory usage hint that guides a GPU driver to control data movement for improving performance, the AccessedBy and ReadMostly are instructions for a series of GPUs, and the described unified memory management policy configuration specifically includes:
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