The project contains sequential and different parallel implementations of kNN algorithm.
The project was created for the needs of master's thesis of Computer Science studies.
The aim of the project was to compare performance of sequential and parallel implementations, depending on threads and processes number used for computing, with the use of different approaches and technologies.
The projects contains a few kNN implementations:
- Sequential implementation - using pure C++
- Multithreaded implementation with the use of OpenMP
- Parallel implementation with the use of OpenMPI
- Massive parallel implementation with use of CUDA
- Visual Studio with C++17 support or higher
- OpenMP support enabled in Visual Studio (for kNN-OpenMP project)
- OpenMPI implementation installed e.g. Microsoft MPI
- CUDA installed (+ CUDA capable graphic card)
$ git clone https://github.com/marmal95/kNN.gitThe Visual Studio solution contains four projects inside - responding four implementations mentioned in Implementations section.
Build whole solution by choosing:
Build > Build Solution
from top menu, or right-click specific project in Solution Explorer and choose:
Build
Right-click on chosen project in Solution Explorer view and click Set as Startup Project.
Click F5 or choose Debug >> Start Debugging from top menu.
Preferred number of theads in OpenMP implementation used for computing may be changed with function call:
omp_set_num_threads(NUM_THREADS)
which is called at the beginning of main() function.
Preferred number of processes is passed as parameter to mpiexec command.
The value may be changed in Visual Studio in: Project > Properties > Configuration Properties > Debugging.
Command mpiexec.exe
Command Arguments -n 4 "$(TargetPath)"
Preferred size of grid used for computing is specified inside CudaAlgorithm.cu file in kNN-CUDA project.
knnOnCuda<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>
Inside that call the number of blocks and number of threads per each block is specified.