Design of High-Performance Computing System for Big Data Analytics

Abstract

The design of a high-performance computing (HPC) system for big data analytics involves selecting hardware and software components that can handle the massive amounts of data generated by big data applications, while ensuring efficient processing and storage of the data. The hardware components of an HPC system typically include high-end servers with powerful processors, large amounts of memory, and high-speed storage devices such as solid-state drives (SSDs) or hard disk drives (HDDs) configured in a parallel or distributed architecture. In addition, specialized hardware such as graphical processing units (GPUs) or field-programmable gate arrays (FPGAs) can be used to accelerate the processing of certain types of data. The software components of an HPC system include the operating system, middleware, and applications. The operating system should be optimized for HPC workloads, with low overhead and high scalability. Middleware such as MPI (Message Passing Interface) can be used to facilitate communication between nodes in a distributed computing environment. Finally, applications should be designed to take advantage of the parallel and distributed processing capabilities of the HPC system, with efficient algorithms and optimized data structures. This paper proposes a design of high-performance computing system for Big Data Analytics.