A fast non-volatile memory aware algorithm for generating random scale-free networks.
Journal
2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017
Pages
787-796
Date Issued
2017
Author(s)
Abstract
In this paper, we propose a method to realize the Barabási-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 10 2 nodes to 10 8 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.
SDGs
Type
conference paper