Abstract
Parallelization has become a universal technique for computing an intensive scientific simulation to shorten the execution time of complex problems. It consists of bringing together the power of several thousand processors to perform complex calculations at high speed. The choice of the runtime environment to execute parallel programs significantly influences the execution time. For this reason, this article aims to materialize the impact of computing architectures on the performance of parallel implementations. To better achieve this contribution, we have implemented the heat equation executed on CUDA platform and we have compared the results with those of SkelGIS implementation from the literature. Through the results of the experiments, we demonstrated that the execution time of the CUDA implementation on graphics processing unit (GPU) is almost 100X faster for very large meshes compared to the other implementations.
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