BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20131119T173000Z
DTEND:20131119T180000Z
LOCATION:401/402/403
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: OPTICS is a hierarchical density-based data clustering algorithm that discovers arbitrarily-shaped clusters and eliminates noise using adjustable reachability distance thresholds. Parallelizing OPTICS is challenging as the algorithm exhibits a strongly sequential data access order. We present a scalable parallel OPTICS algorithm (POPTICS) designed using graph algorithmic concepts. To break the data access sequentiality, POPTICS exploits the similarities between the OPTICS algorithm and Prim's Minimum Spanning Tree algorithm. Additionally, we use the disjoint-set data structure to achieve a high parallelism for distributed cluster extraction. Using high dimensional datasets containing up to a billion floating point numbers, we show scalable speedups of up to 27.5 for our OpenMP implementation on a 40-core shared-memory machine, and up to 3,008 for our MPI implementation on a 4,096-core distributed-memory machine. We also show that the quality of the results given by POPTICS are comparable to those given by the classical OPTICS algorithm.
SUMMARY:Scalable Parallel OPTICS Data Clustering Using Graph Algorithmic Techniques
PRIORITY:3
END:VEVENT
END:VCALENDAR