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Big and complex applications need many resources and long computation time to execute sequentially. In this scenario, all application's processes are handled in sequential fashion even if they are independent of each other. In high- performance computing environment, multiple processors are available to running applications in parallel. So mutually independent blocks of codes could run in parallel. This approach not only increases the efficiency of the system without affecting the results but also saves a significant amount of energy. Many parallel programming models or APIs like Open MPI, Open MP, CUDA, etc. are available to running multiple instructions in parallel. In this paper, the efficiency and energy consumption of two known tasks i.e. matrix multiplication and quicksort are analyzed using different parallel programming models and a multiprocessor machine. The obtained results, which can be generalized, outline the effect of choosing a programming model on the efficiency and energy consumption when running different codes on different machines.

The Effect of Parallel Programming Languages on the Performance and Energy Consumption of HPC Applications