A fast non-volatile memory aware algorithm for generating random scale-free networks

Abstract

In this paper, we propose a method to realize the Barabasi-Albert (BA) model for generating large scale-free networks with preferential attachment. To our knowledge, the existing implementations of the BA model are still not very efficient because they failed to manage the temporary data of the network generating process properly by ignoring the inherent power law degree distribution property. To address this problem, we propose to leverage data structures including a prefix sum max heap and index arrays, which can competently manage nodes with different amount of connections. The proposed method is also friendly to the computing system with non-volatile memory (NVM) as main memory. Reducing long-latency write operations is the key to improve the efficiency of NVM, while the proposed method can ultimately save not only read operations but also significant amount of writes. We compare our proposed method with the baseline methods by generating networks of size from 102 nodes to 108 nodes. Experiment results show that the proposed method can save up to 50% of write counts. Furthermore, when using the phase change memory, a new byte-addressable non-volatile memory, as main memory, the proposed method can be almost two times faster.