Abstract
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.
Highlights
With the addition of multiple cores, the capability of chips to process multiple instructions simultaneously has increased the performance
This kind of performance analysis studies help to improve the performance of the software application and to provide an optimal solution
The following section presents the results obtained by running the matrix multiplication program for different matrix sizes and using different programming models
Summary
With the addition of multiple cores, the capability of chips to process multiple instructions simultaneously has increased the performance. High-performance computing provides boost in performance but at some stages, it requires more resources to increase the performance. To provide an optimal solution which could be running efficiently and consumes fewer resources, like energy etc. The performance of the computing system must be analyzed. Multiple performance analysis tools could be used to test the performance of different software applications [1]. This kind of performance analysis studies help to improve the performance of the software application and to provide an optimal solution. Tools that are utilized for the performance analysis of HPC applications use different approaches for the analysis purposes [2]
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