research-article

An efficient hardware supported and parallelization architecture for intelligent systems to overcome speculative overheads

Authors:

Sudhakar Kumar,

Sunil K. Singh,

Naveen Aggarwal,

Wadee Alhalabi,

Shahab S. BandAuthors Info & Claims

International Journal of Intelligent Systems, Volume 37, Issue 12

Pages 11764 - 11790

https://doi.org/10.1002/int.23062

Published: 29 December 2022 Publication History

Abstract

In the last few decades, technology advancements have paved the way for the creation of intelligent and autonomous systems that utilize complex calculations which are both time‐consuming and central processing unit intensive. As a consequence, parallel processing systems are gaining popularity to enhance overall computer performance. Programmers should be able to efficiently utilize available hardware resources with parallelization in an ideal world. Through the automatic parallelization of sequential code, multithreading can be executed without extra supervision. However, a wide range of software dependencies prevents this from being feasible. An architectural framework for speculative parallelization along with an efficient memory analysis and computational algorithms for the code generation are proposed that can provide optimal performance. Furthermore, a suitable support of hardware design as a runtime library to the proposed architectural framework is presented which can be used to recover misspeculated results during execution to minimize speculative parallelism overhead. The implementation makes use of the Low‐Level Virtual Machine compiler infrastructure and is tested on numerous benchmarks, thus making it highly scalable in terms of programming languages and architectures. According to our experimental results, there is significant potential for speedup increase. In comparison to the overall function speedup, that is, geomean speedup of 5.2× approximately when using the proposed architecture without hardware support, the proposed architectural framework and algorithm with hardware support give an average geomean speedup of 7.0× approximately on the given benchmark which is written in C/C++.

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cover image International Journal of Intelligent Systems

International Journal of Intelligent Systems Volume 37, Issue 12

December 2022

2488 pages

ISSN:0884-8173

DOI:10.1002/int.v37.12

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© 2022 Wiley Periodicals LLC.

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John Wiley and Sons Ltd.

United Kingdom

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Published: 29 December 2022

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Cited By

Sharma ASingh SKumar SThakur RGupta BArya V(2024)IoT-enabled smart farming with Industry 5.0Journal of High Speed Networks10.3233/JHS-23025830:3(477-496)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JHS-230258
Kumar RSingh SLobiyal DKumar SJawla S(2024)Security Metrics and Authentication-based RouTing (SMART) Protocol for Vehicular IoT NetworksSN Computer Science10.1007/s42979-023-02566-75:2Online publication date: 27-Jan-2024
https://dl.acm.org/doi/10.1007/s42979-023-02566-7
Martínez MFraguela BCabaleiro JRivera F(2024)A new thread-level speculative automatic parallelization model and library based on duplicate code executionThe Journal of Supercomputing10.1007/s11227-024-05987-080:10(13714-13737)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11227-024-05987-0
Goyal SKumar SSingh SSarin SPriyanshu Gupta BArya VAlhalabi WColace F(2024)Synergistic application of neuro-fuzzy mechanisms in advanced neural networks for real-time stream data flux mitigationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09938-y28:20(12425-12437)Online publication date: 1-Oct-2024
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