This paper is published in Volume-4, Issue-6, 2018
Area
High Performance Data Analytics
Author
Nikita Mutreja
Co-authors
Sanyam Jhamb
Org/Univ
Amity University, Noida, Uttar Pradesh, India
Pub. Date
16 November, 2018
Paper ID
V4I6-1139
Publisher
Keywords
High performance computing, Big data, Job scheduling, Hadoop, YARN, Converging paradigms, MapReduce, HDFS

Citationsacebook

IEEE
Nikita Mutreja, Sanyam Jhamb. High performance computing v/s big data, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Nikita Mutreja, Sanyam Jhamb (2018). High performance computing v/s big data. International Journal of Advance Research, Ideas and Innovations in Technology, 4(6) www.IJARIIT.com.

MLA
Nikita Mutreja, Sanyam Jhamb. "High performance computing v/s big data." International Journal of Advance Research, Ideas and Innovations in Technology 4.6 (2018). www.IJARIIT.com.

Abstract

Simulation has become a “must have” item in the technology toolbox for manufacturers who wish to optimize the product development process, reduce production costs, and speed-time-to market. Along with Big Data insights and HPC solutions, simulation can enhance the product design process by leveraging to drive product innovation, improve time to time value. These models (Big data and HPC) provide the advanced capabilities that are needed by the manufacturers to get to the market faster than their competition. In this paper, we analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Big data paradigm. Further, the characteristics of the two paradigms have been discussed, along with comparisons and contrasts of the two approaches. It also covers the scope of these paradigms and sheds light upon the specific workloads that utilize them. At last, we discuss the convergence of both paradigms; the best of both world’s approach.
Paper PDF