Introduction to Dataflow Computing

View Sample PDF

Author(s): Nenad Korolija (School of Electrical Engineering, University of Belgrade, Serbia), Jovan Popović (Microsoft Development Center, Serbia)and Miroslav M. Bojović (School of Electrical Engineering, University of Belgrade, Serbia)
Copyright: 2021
Pages: 10
Source title: Handbook of Research on Methodologies and Applications of Supercomputing
Source Author(s)/Editor(s): Veljko Milutinović (Indiana University, Bloomington, USA)and Miloš Kotlar (University of Belgrade, Serbia)
DOI: 10.4018/978-1-7998-7156-9.ch006

Keywords: Computer Science & IT / Grid & High Performance Computing / High Performance Computing / Information Science Reference

Purchase

View Introduction to Dataflow Computing on the publisher's website for pricing and purchasing information.

Abstract

This chapter presents the possibilities for obtaining significant performance gains based on advanced implementations of algorithms using the dataflow hardware. A framework built on top of the dataflow architecture that provides tools for advanced implementations is also described. In particular, the authors point out to the following issues of interest for accelerating algorithms: (1) the dataflow paradigm appears as suitable for executing certain set of algorithms for high performance computing, namely algorithms that work with big data, as well as algorithms that include a lot of repetitions of the same set of instructions; (2) dataflow architecture could be configured using appropriate programming tools that can define hardware by generating VHDL files; (3) besides accelerating algorithms, dataflow architecture also reduces power consumption, which is an important security factor with edge computing.

The IRMA Community

Research IRM

Introduction to Dataflow Computing

Purchase

Abstract

Related Content

IRMA Sponsors