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Exascale Computing, GPU, CUDA, OpenMP, Parallelism, High Performance Computing (HPC).

Exascale computing refers to a computing system which is capable to at least one exaflop in next couple of years. Many new programming models, architectures and algorithms have been introduced to attain the objective for exascale computing system. The primary objective is to enhance the system performance. In modern/super computers, GPU is being used to attain the high computing performance. However, it’s the objective of proposed technologies and programming models is almost same to make the GPU more powerful. But these technologies are still facing the number of challenges including parallelism, scale and complexity and also many more that must be fixed to achieve make computing system more powerful and efficient. In this paper, we have present a testing tool architecture for a parallel programming approach using two programming models as CUDA and OpenMP. Both CUDA and OpenMP could be used to program shared memory and GPU cores. The object of this architecture is to identify the static errors in the program that occurred during writing the code and cause absence of parallelism. Our architecture enforces the developers to write the feasible code through we can avoid from the essential errors in the program and run successfully.