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DTSTAMP:20260522T150124Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181112T110000
DTEND;TZID=America/Chicago:20181112T113000
UID:submissions.supercomputing.org_SC18_sess151_ws_mlhpce113@linklings.com
SUMMARY:Large-Scale Clustering Using MPI-Based Canopy
DESCRIPTION:Thomas Heinis (Imperial College, London)\n\nAnalyzing massive 
 amounts of data and extracting value from it has become key across differe
 nt disciplines. Many approaches have been developed to extract insight fro
 m the plethora of data available.  As the amount of data grow rapidly, how
 ever, current approaches for analysis struggle to scale. This is particula
 rly true for clustering algorithms which try to find patterns in the data.
  \n\nA wide range of clustering approaches has been developed in recent ye
 ars. What they all share is that they require parameters (number of cluste
 rs, size of clusters etc.) to be set a priori. Typically these parameters 
 are determined through trial and error in several iterations or through pr
 e-clustering algorithms. Several pre-clustering algorithms have been devel
 oped, but similarly to clustering algorithms, they do not scale well for t
 he rapidly growing amounts of data.\n\nIn this paper, we thus take one suc
 h pre-clustering algorithm, Canopy, and develop a parallel version based o
 n MPI. As we show, doing so is not straightforward and without optimizatio
 n, a considerable amount of time is spent waiting for synchronization, sev
 erely limiting scalability. We thus optimize our approach to spend as litt
 le time as possible with idle cores and synchronization barriers. As our e
 xperiments show, our approach scales near linear with increasing dataset s
 ize.\n\nTag: Deep Learning, Machine Learning\n\nRegistration Category: Wor
 kshop Reg Pass\n\n
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