Abstract
Minimizing execution time, energy consumption, and network load through scheduling algorithms is challenging for multi-processor-on-chip (MPSoC) based network-on-chip (NoC) systems. MPSoC based systems are prevalent in high performance computing systems. With the increase in computing capabilities of computing hardware, application requirements have increased many folds, particularly for real world scientific applications. Scheduling large scientific workflows consisting hundreds and thousands of tasks consume significant amount of time and resources. In this article, energy aware parallel scheduling techniques are presented primarily aimed at reducing the algorithm execution time while considering network load. Experimental results reveal that the proposed parallel scheduling algorithms achieve significant reduction in execution time.
Highlights
Multi-processor system-on-chip MPSoC) technology allows multiple processing elements of varying capabilities and capacities to be embedded onto a single chip
RESULT AND DISCUSSION This section compares the performance of proposed sequential algorithms namely HEFT ranking base level (HRBL) and E3FT with the existing BL and base level task stealing (BLTS) techniques proposed in [28] in the literature
The metrics used to measure the performance of the algorithms include: (a) execution time (b) energy consumption, and (c) network load
Summary
Multi-processor system-on-chip MPSoC) technology allows multiple processing elements of varying capabilities and capacities to be embedded onto a single chip. Improving energy efficiency of MPSoCs is important to realize the sustainable adoption of these systems in commercial and household devices [7]. Improving MPSoCs efficiency is critical in achieving the sustainable deployment of these systems in industrial and residential devices that deals with large workflows. The core count of MPSoC is expected to hit hundreds, or even thousands by 2025 [12]. This growth comes with a cost, i.e., increased execution time and energy consumption. Most scheduling algorithms are developed using a linear or sequential approach, which consumes considerable time and, increases energy consumption of MPSoCs when applied to large workflows with hundreds of thousands of tasks.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
