Distributed vertex-centric model has been recently proposed for large-scale graph processing. Due to the simple but efficient programming abstraction, similar graph computing frameworks based on GPUs are gaining more and more attention. However, prior works of GPU-based graph processing suffer from load imbalance and irregular memory access because of the inherent characteristics of graph applications. In this paper, we propose a generalized graph computing framework for GPUs to simplify existing models but with higher performance. In particular, two novel algorithmic optimizations, lightweight approximate sorting and data layout transformation, are proposed to tackle the performance issues of current systems. With extensive experimental evaluation under a wide range of real world and synthetic workloads, we show that our system can achieve 1.6× to 4.5× speedups over the state-of-the-art.

Summary With the rapid development of smartphones in recent years, we have witnessed an exponential growth of the number of mobile apps. Considering the security and management issues, network operators need to have a clear visibility into the apps running in the network. To this end, this paper presents a novel approach to generating the fingerprints for mobile apps from network traffic. The fingerprints that characterize the unique behaviors of specific mobile apps can be used to identify mobile apps from the real network traffic. In order to handle the large volume of traffic efficiently, we use non‐negative matrix factorization (NMF) to perform traffic analysis to cluster similar network traffic into groups. Then, access patterns of individual apps that are extracted from each group can be used as fingerprints distinguishing apps from others uniquely. The experimental evaluations show that the proposed approach can identify the mobile apps from random and mixed network traffic with high precision. Copyright © 2015 John Wiley & Sons, Ltd.