# Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications
## 2 Background and Motivation  
Since its inception in 1991, HPCC provided vital enabling technologies and disseminated progress in the field to extend the lead in the field of HPC. Later, more agencies joined in a coordinated effort to develop HPC. Most development and progress work in HPC was made available on the HPCC website as annual reports. This initiative led to significant improvements in HPC research.  
The HPC cluster is used in implementations and experiments to compute the simulated Gaussian density function to solve a density estimation problem based on shared memory and the parallel execution of the kernel density estimation algorithm on a graphic processing unit (GPU)-enabled system [4; 7], the OpenMP library [8]. The study results reveal significant improvement in the time taken by the HPC cluster in estimating the density function. Although GPU-based computing provides easy access to higher teraflops with fewer machines, this setup is only suitable for parallel jobs, whereas fine-grain parallelism, synchronization of threads, and programming GPU-enabled systems are complex tasks. Therefore, several studies have been performed on the Intel Xeon processor-based HPC cluster. These clusters have the advantages of being easy to program, supporting SIMD and MIMD models, and providing low-cost alternatives to a few teraflops with the ease of scalability.  
The floating-point operations (FLOPs) [9; 10; 11] are used in scientific computing community to measure the processing power of individual computers, different types of HPC, Grid, and supercomputing facilities. China has made significant progress in developing HPC systems in recent years. Since the development of the supercomputer by their national defense university, the nation's first win of the Gordon Bell Prize at Supercomputing 2016 (SC16) also represents an accomplishment in HPC applications. As a result, various academic institutions, private companies, and research groups have collaborate and contributed to the development of HPC on their premises [3; 10; 12; 13; 14]. Major applications of HPC are in data storage and analysis, data mining, simulation and modeling, scientific calculations, bioinformatics, big data challenges, and complex visualizations.  
However, the purchase of a supercomputer for academic and collaborative research may cost an investment of millions of dollars and therefore most universities avoid directly purchasing the supercomputers. But to provide a platform for the students and researchers to work on the most challenging scientific problems related to different fields such as computational fluid dynamics, medical imaging, graphics, higher dimensional visualization, big data, parallel machine learning, and data mining multidisciplinary optimization. Many universities have implemented low-cost supercomputing using clusters of Intel processors. Commodity computers fail to process complex jobs with very large data and computations as memory requirements and millions of times complex function evaluations. To solve bigger problems, scientists have invented cost-effective solutions to aggregate individual gigaflops of individual computers by clustering and facilitating the high-speed I/O and communication to provide a platform that performs the large-scale experiments. In this study, most reviewed research ideas are taken from HPC applications in scientific computing because the objective is to provide an HPC-based supercomputing infrastructure for various scientific computing applications.