Parallel and Distributed Systems project for the a.y. 2020/2021.
Given a set of points in a 2D space, we require to compute in parallel for each one of the points in the set of points the set of k closest points. Point i is the point whose coordinates are listed in line i in the file. The input of the program is a set of floating-point coordinates (one per line, comma separated) and the output is a set of lines each hosting a point id and a list of point ids representing its KNN set ordered with respect to distance.
To generate the points that will be used during the KNN use the following command:
python generate_points.py --n 100000 --file datasets/points_file.txt --lbound 0 --ubound 10
It's suggested to save the datasets in the respective directory for a better organization.
Compile with make, then run the following commands as you prefer:
./knn_sequential datasets/points_dataset.txt
./knn_parallel datasets/points_dataset.txt n_workers
./knn_ff datasets/points_dataset.txt n_workers
The results will be saved with the name knn_implementation_points_workers.txt.
If you want to run more versions at the same time for more runs use the following command:
python --file datasets/points_10k.txt --k 10 --nw 256 --runs 10 --execute spf
The last argument determines which versions to run (s for sequential, p for parallel and f for fastflow), then you can use knn_plot.py to plot the results (beware that you need to manually change them).
To plot the results use knn_plot.py in the following way:
python knn_plot.py --s --scalab --e --exectime --compare
You can choose which kind of metrics to plot by typing --s for the speedup, --scalab for the scalability, e for efficiency and --exectime for the execution time. Then you can also --compare to plot the comparison for the metrics chosen before.