Energy efficient real-time scheduling algorithm for mixed task set on multi-core processors

Digalwar, Mayuri; Gahukar, Praveen; Raveendran, Biju K.; Mohan, Sudeept

doi:10.1504/ijes.2017.088037

Cited by 4 publications

(1 citation statement)

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“…High performance computing (HPC) constantly evolves based on novel computing architectures, scalable algorithms (Müller et al, 2018), and energy efficient solutions (O'Brien et al, 2017;Kaushik and Vidyarthi, 2018;Xiong et al, 2017;Digalwar et al, 2017) for solving science problems, year by year. These updates have consistently retained HPC researchers over decades for solving the emerging challenges and fine-tuning the available solutions at various levels of implementing scientific applications.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement Options of Scientific Applications on XeonPhi KNL Architectures

Benedict

2018

IJKEDM

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Intel's recent manycore processor KNights Landing (KNL) promises high performance for scientific applications. Careful tuning for the complex chip architecture is required to efficiently exploit the chip's hardware resources. This paper describes performance improvement techniques and demonstrates their effectiveness for scientific applications. Experiments were conducted with some of the National Aeronautics and Space Administration (NASA's) advanced supercomputing (NAS) parallel benchmarks, and the effectiveness of: 1) advanced vector extensions (AVX-512) vectorisation support; 2) manycore threading support; 3) the utilisation of thread affinities for different KNL modes, was analysed.

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Section: Introductionmentioning

confidence: 99%

Performance Improvement Options of Scientific Applications on XeonPhi KNL Architectures

Benedict

2018

IJKEDM

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Hybrid fuzzy-based Deep Remora Reinforcement Learning Based Task Scheduling in Heterogeneous Multicore-processor

Gupta¹,

Agarwal

2022

Microprocessors and Microsystems

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A Migration Aware Scheduling Technique for Real-Time Aperiodic Tasks Over Multiprocessor Systems

et al. 2019

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Multi-processor systems consist of more than one processor and are mostly used for computationally intensive applications. Real-time systems are those systems that require completing execution of tasks within a pre-defined deadline. Traditionally, multiprocessor systems are given attention in periodic models, where tasks are executed at regular intervals of time. Gradually, as maturity in a multiprocessor design had increased; their usage has become very common for real-time systems to execute both periodic and aperiodic tasks. As the priority of an aperiodic task is usually but not essentially greater than the priority of a periodic task, they must be completed within the deadline. There is a lot of research works on multiprocessor systems with scheduling of periodic tasks, but the task scheduling is relatively remained unexplored for a mixed workload of both periodic and aperiodic tasks. Moreover, higher energy consumption is another main issue in multiprocessor systems. Although it could be reduced by using the energy-aware scheduling technique, the response time of aperiodic tasks still increases. In the literature, various techniques were suggested to decrease the energy consumption of these systems. However, the study on reducing the response time of aperiodic tasks is limited. In this paper, we propose a scheduling technique that: 1) executes aperiodic tasks at full speed and migrates periodic tasks to other processors if their deadline is earlier than aperiodic tasks-reduces the response time and 2) executes aperiodic tasks with lower speed by identifying appropriate processor speed without affecting the response time-reduces energy consumption. Through simulations, we demonstrate the efficiency of the proposed algorithm and we show that our algorithm also outperforms the well-known total bandwidth server algorithm. INDEX TERMS Real-time systems, multi-processor scheduling, energy consumption, performance, dynamic voltage and frequency scaling.

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Energy efficient real-time scheduling algorithm for mixed task set on multi-core processors

Cited by 4 publications

References 0 publications

Performance Improvement Options of Scientific Applications on XeonPhi KNL Architectures

Performance Improvement Options of Scientific Applications on XeonPhi KNL Architectures

Hybrid fuzzy-based Deep Remora Reinforcement Learning Based Task Scheduling in Heterogeneous Multicore-processor

A Migration Aware Scheduling Technique for Real-Time Aperiodic Tasks Over Multiprocessor Systems

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