Prediction of multicore CPU performance through parallel data mining on public datasets

Abstract

In the present scenario, high-performance computing needs more attention towards multicore computing. While designing the CPU, we need to consider hardware for processing speed, cache bandwidth, minimum memory requirements, etc. So for the selection of the best combination of CPU data mining tools may play an important role. In this era, data mining attracts more interest on parallel computing to enhance the performance of multicores. This paper demonstrates a parallel strategy similar to the traditional parallel programming paradigm to improve the performance of multicores by using a data mining approach. We have selected one approach EM with Gaussian and analyze the impact of its parallel execution on selected multicore clusters obtained through data mining. We have also evaluated our finding with a virtual environment of having 32 different families of CPUs, showing a speedup of up to ≈1.02x. This paper considers data clusters, cache mapping techniques and ranking of MPI programming techniques.