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Sequential and Parallel(using Open MP and Pthreads) Implementations(c++) of the K Means Clustering Algorithm and visualizing the results for a comparative study of the Speedup and Efficiency achieved in 3 different implementations

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vinayak1998/Parallel-K-Means-Clustering

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Parallel-K-Means-Clustering

Sequential and Parallel(using Open MP and Pthreads) Implementations(c++) of the K Means Clustering Algorithm and visualizing the results for a comparative study of the Speedup and Efficiency achieved in 3 different implementations

Execution

  • Sequential

    • cd into Sequential folder
    • g++ main_sequential.c lab1_io.c Kmeans-Sequential.cpp -fopenmp -o seq.out
    • ./seq.out 4 sample_dataset_50000_4.txt b.txt c.txt
    • 4 is for the number of clusters. Can be changed.
    • To Visualize the results :- python visualise.py b.txt
  • OpenMP

    • cd into OpenMP folder
    • g++ main_omp.c lab1_io.c Kmeans-OpenMP.cpp -fopenmp -o omp.out
    • ./omp.out 4 2 sample_dataset_5000_3.txt b.txt c.txt
    • 4 is for the number of clusters and 2 is for the number of threads. Both can be changed.
    • To Visualize the results :- python visualise.py b.txt
  • P-Threads

    • cd into P-Threads folder
    • g++ lab1_io.c main_pthread.c Kmeans-Pthreads.cpp -o ptry.out -fopenmp
    • ./ptry.out 4 2 sample_dataset_50000_4.txt b.txt c.txt
    • 4 is for the number of clusters and 2 is for the number of threads. Both can be changed.
    • To Visualize the results :- python visualise.py b.txt

Sample Visualization

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Sequential and Parallel(using Open MP and Pthreads) Implementations(c++) of the K Means Clustering Algorithm and visualizing the results for a comparative study of the Speedup and Efficiency achieved in 3 different implementations

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