Finding the best network for laying renewable energy based solar panel roads: A GPU parallel algorithm implemented on CUDA - Kapur, Aayush; Gupta, Nirut (International Journal of Computer Sciences and Engineering, 2019)
The objective of this paper is implementation of ACO algorithm on GPU to combat real life problems of road network identification along with an application focusing on renewable energy. GPUs are specialized microprocessors that accelerates graphics operation. Parallel processing is required when we consider a heavy code with so much of similar iterations. CUDA is NVIDIA’s architecture for parallel computing that is used for extensive parallel computing and increases the performance by employing the GPU (Graphical Processing Unit). We have Ant colony optimisation algorithm implementation that is a bit different than others. Also, we compare it with the sequential code and the results are that it is very fast as compared to sequential code. To deal with the execution of optimisation we will propose two different approaches, one will be the serial approach of the ACO algorithm to generate the network and other will be GPU / CUDA based approach. We will compare the execution time in both the cases and then find out the speed up. An applicability of this approach is for generating the best possible road network for city coordinates where we try to get the network with least cost. This is of immense applicability for developing countries where road networks are upcoming.