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A project that involved C programming. Where we made use of parallel implementations of OpenMPI and Cuda. We compared the performance of these two and attempted to perform K Means and Fuzzy C Means clustering on synthetic data.

C 84.19% Makefile 0.02% Cuda 0.41% C++ 15.32% Shell 0.06%
cuda cuda-programming openmpi openmpi-cpu-clusters parallel-programming k-means-clustering fuzzy-cmeans-clustering c high-performance-computing

hpc-project's Introduction

HPC-Project

HPC-Project

The project's code segments are fairly well commented. This is largely only intended to be used as a reference. The focus of this project was to showcase the performance differnces in Fuzzy C-Means clustering and K-Means clustering which made use of OpenMPI and CUDA in each implementation. Thus, comparing the benefits of a chosen framework. It should be noted, this made use of global memory in the case of CUDA which is sub-optimal for this problem but served the purpose of a basic CUDA implementation.

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