Performance Model of HPC Application On CPU-GPU Platform

In recent years, the world of high-performance computing has been developing rapidly with enormous efforts in the integration of information technology and research. The emergence of CPU-GPU platform computing has made this possible in a very efficient manner. Nowadays, the graphic processing unit (GPU) delivers much better performance than the CPU, because of a few cores with lots of cache memory on the CPU that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously. The CPU-GPU hybrid platform is becoming increasingly important in high-performance computing (HPC) domains such as deep learning, artificial intelligence, etc., because of its tremendous computing power. In this work, we have proposed a performance model to accelerate the performance of HPC applications on a hybrid CPU-GPU platform. We have tested and analyzed the proposed performance model using different HPC benchmark applications such as Merge sort and Matrix multiplication on different platforms such as sequential, OpenMP, MPI in a single system, MPI in the cluster, and CUDA. We have observed that parallel computing in a shared and distributed memory architecture gives better performance than sequential computing. After analyzing we have represented it in the terms of graphs for a better view of the results.