Exhaustive community enumeration on a cluster
Por:
Trefftz C., McGuire H., Kurmas Z., Scripps J., Pineda J.D.
Publicada:
1 ene 2018
Resumen:
A parallelization based on MPI and OpenMP of an algorithm that evaluates and counts all the possible communities of a graph is presented. Performance results of the parallelization of the algorithm obtained on a cluster of workstations are reported. Load balancing was used to improve the speedups obtained on the cluster. Two different kinds of load balancing approaches were used: One that involved only MPI and a second one in which MPI and OpenMP were combined. The reason for the load imbalance is described. © 2018 IEEE.
Filiaciones:
Trefftz C.:
School of Computing, Grand Valley State University, Allendale, MI 49401, United States
McGuire H.:
School of Computing, Grand Valley State University, Allendale, MI 49401, United States
Google, United States
Kurmas Z.:
School of Computing, Grand Valley State University, Allendale, MI 49401, United States
Scripps J.:
School of Computing, Grand Valley State University, Allendale, MI 49401, United States
Pineda J.D.:
Centro de Cómputo Apolo, Universidad EAFIT, Medellín, Colombia
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