Jyothish Soman

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 (

A fast GPU algorithm for graph connectivity

352

SOME GPU ALGORITHMS FOR GRAPH CONNECTED COMPONENTS AND SPANNING TREE

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

Performance evaluation of building detection and digital surface model extraction algorithms: Outcomes of the PRRS 2008 Algorithm Performance Contest

100

Discrete range searching primitive for the GPU and its applications

M edges), using Power 6 architecture with

Efficient Discrete Range Searching primitives on the GPU with applications

32

Graph theoretic approach for studying correlated motions in biomolecules

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

REPAIR: Hard-error recovery via re-execution

8\times

Maximizing TTI RTM Throughput for CPU+GPU

improvement over the Power 6 performance on

Grabfast: A CUDA based GPU accelerated fast short sequence alignment algorithm

40