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UID:submissions.supercomputing.org_SC23_sess454_ws_eduhpca104@linklings.co
 m
SUMMARY:k-Nearest Neighboor with Map Reduce MPI
DESCRIPTION:Erik Saule (University of North Carolina, Charlotte)\n\nThis i
 s the summary of a peachy parallel assignment centered on classifying obje
 cts based on a database of pre-classified objects; in particular this assi
 gnment uses the k-Nearest Neighbors method. With the increase of popularit
 y of data science and machine learning, data science assignments have beco
 me more engaging for students. In this particular case, we rely on existin
 g databases of machine learning problems to provide real world application
 s of the k-nearest neighbor algorithm. The databases being fairly large ma
 kes the runtime of the algorithm fairly slow, which makes the consideratio
 n of parallel computing natural. This incarnation of the assignment uses M
 ap Reduce MPI and was used in an upper division parallel computing class. 
  However the assignment can be adapted as a CS1/CS2 assignment or as a Dat
 a Structures assignment.\n\nTag: Education, Heterogeneous Computing, Repro
 ducibility, State of the Practice, Sustainability\n\nRegistration Category
 : Workshop Reg Pass\n\nSession Chairs: David P. Bunde (Knox College); Sush
 il K. Prasad (University of Texas at San Antonio); Satish Puri (Missouri U
 niversity of Science and Technology); Apan Qasem (Texas State University);
  and George K. Thiruvathukal (Loyola University, Chicago; Argonne National
  Laboratory (ANL))\n\n
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