Abstract

Cluster algorithms have application in diverse areas, including statistical mechanics of polymer solutions, spin models in physics, and the study of ecological systems. Most parallel cluster labeling algorithms are designed for SIMD and MIMD multiprocessors and based on relaxation methods. We present a parallel 3-D cluster labeling algorithm based on mapping tables, for distributed memory environments. The proposed algorithm focuses on minimizing interprocess communication to enhance execution performance on workstation networks. We implemented the algorithm with the aid of theEcliPSeparallel replication toolkit, exploiting special tree-combining and data reduction features of the system. We report on performance results for experiments conducted on workstation clusters.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.