Ankur Narang

Fast Community Detection Algorithm with GPUs and Multicore Architectures

In this paper, we present the design of a novel scalable parallel algorithm for community detection optimized for multi-core and GPU architectures. Our algorithm is based on label propagation, which works solely on local information, thus giving it the scalability advantage over conventional approaches. We also show that weighted label propagation can overcome typical quality issues in communities detected with label propagation. Experimental results on well known massive scale graphs such as Wikipedia (100M edges) and also on RMAT graphs with 10M - 40M edges, demonstrate the superior performance and scalability of our algorithm compared to the well known approaches for community detection. On the \textit{hep-th} graph (

High throughput data redundancy removal algorithm with scalable performance

352

Distributed scalable collaborative filtering algorithm

K edges) and the \textit{wikipedia} graph (

Highly scalable parallel collaborative filtering algorithm

100

Highly scalable algorithm for distributed real-time text indexing

M edges), using Power 6 architecture with

Dynamic distributed scheduling algorithm for state space search

32

High Performance Offline and Online Distributed Collaborative Filtering

cores, our algorithm achieves one to two orders of magnitude better performance compared to the best known prior results on parallel architectures with similar number of CPUs. Further, our GPGPU based algorithm achieves

Predictive Caching Framework for Mobile Wireless Networks

8\times

Streaming quotient filter: a near optimal approximate duplicate detection approach for data streams

improvement over the Power 6 performance on

Real-time approximate Range Motif discovery & data redundancy removal algorithm

40