The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: EduHPC-23: Workshop on Education for High Performance Computing
Authors: Erik Saule (University of North Carolina, Charlotte)
Abstract: This is the summary of a peachy parallel assignment centered on classifying objects based on a database of pre-classified objects; in particular this assignment uses the k-Nearest Neighbors method. With the increase of popularity of data science and machine learning, data science assignments have become more engaging for students. In this particular case, we rely on existing databases of machine learning problems to provide real world applications of the k-nearest neighbor algorithm. The databases being fairly large makes the runtime of the algorithm fairly slow, which makes the consideration of parallel computing natural. This incarnation of the assignment uses Map 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 Data Structures assignment.
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