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DTSTART;TZID=America/Denver:20231113T170500
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UID:submissions.supercomputing.org_SC23_sess454_ws_eduhpca108@linklings.co
 m
SUMMARY:K-means Clustering:  An Assignment for OpenMP, MPI, and CUDA/OpenC
 L
DESCRIPTION:Diego García-Álvarez (Universidad de Valladolid) and Arturo Go
 nzalez-Escribano (University of Valladolid, Spain)\n\nWe introduce the six
 th example in a series of assignments used in a Parallel Computing course 
 to teach approaches to solving the same problem with different parallel pr
 ogramming models. This assignment is based on the K-means clustering algor
 ithm. The program is intentionally designed to be straightforward and easi
 ly understandable for students, while also providing specific parallelizat
 ion and optimization opportunities. It is a simpler example than the previ
 ously presented assignments in this series, focusing mainly on key base co
 ncepts that many students find complex to apply in a practical case: Race-
 conditions, reductions, and collective operations. It proposes a clear and
  guided parallelization and optimization strategy across the different pro
 gramming models.  This assignment can be used  to establish a solid founda
 tion before tackling more advanced concepts or parallel structures.  This 
 assignment was successfully used as a practical assignment in a Parallel C
 omputing course in the third year of a Computer Engineering degree.\n\nTag
 : Education, Heterogeneous Computing, Reproducibility, State of the Practi
 ce, Sustainability\n\nRegistration Category: Workshop Reg Pass\n\nSession 
 Chairs: David P. Bunde (Knox College); Sushil K. Prasad (University of Tex
 as at San Antonio); Satish Puri (Missouri University of Science and Techno
 logy); Apan Qasem (Texas State University); and George K. Thiruvathukal (L
 oyola University, Chicago; Argonne National Laboratory (ANL))\n\n
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