High-Performance Computation in Big Data Analytics

Abstract

For many years, big data analytics has relied on High-Performance Computing (HPC) for an efficient analysis. Today data is growing at an accelerated pace, so new types of high-performance computing will be required to access historically unprecedented volumes of data. To identify patterns and new insights, high-performance data analytics combines high-performance computing (HPC) with data analytics. The technique of quickly evaluating exceptionally big data sets to identify insights is known as high-performance data analytics. This is accomplished by utilising high-performance computing's parallel processing to execute strong analytic tools. For government and commercial companies that need to integrate high-performance computing with data-intensive analysis, high-performance data analytics infrastructure is a new and rapidly rising sector. As we can see natural hazards are unpredicted and affects our day to day work unexpected rainfall. So climate prediction becomes a need for nowadays. For this a large amount of database is needed for storing, maintaining, processing the datasets for performing the prediction in well manner. Separately, big data computing and HPC has progressed throughout time. These two techniques are becoming increasingly dependent on one another for data management and algorithms due to the growth of data and the requirement for machine learning algorithms. For instance, public clouds like Microsoft Azure are enabling artificial intelligence algorithms on big datasets by deploying large-scale Graphical processing Unit (GPU) deployments in HPC clusters and adding high-performance computing instances with InfiniBand. Understanding the evolution of HPC systems and big data help to define the important differences, as well as the goals and architectures that support them. Big data systems have benefitted data management, data querying, and streaming applications.