Energy-Efficient Allocation of Real-Time Applications onto Single-ISA Heterogeneous Multi-Core Processors

Colin, Alexei; Kandhalu, Arvind; Rajkumar, Ragunathan

doi:10.1007/s11265-015-0987-3

Cited by 28 publications

(10 citation statements)

References 21 publications

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“…Chen et al introduced a polynomial-time approximation algorithm to partition tasks on the platform consisting of two processing elements [14]. Colin et al proposed a heuristic by approximating the desired load distribution for heterogeneous systems [15]. Recently, Pagani et al in Ref.…”

Section: Real-time Scheduling On a Fixed Platformmentioning

confidence: 99%

“…All α, β and s are technology-based parameters, which have the same meanings with k, α, β in Ref. [15] respectively. Note that the tasks may have different periods, thus the energy consumption should be calculated based on the minimum repeating interval, the hyper-period time as follows: L = LC M({T i : τ i ∈ Γ}), where LC M is lowest common multiple.…”

Section: System Modelsmentioning

confidence: 99%

“…Note that the tasks may have different periods, thus the energy consumption should be calculated based on the minimum repeating interval, the hyper-period time as follows: L = LC M({T i : τ i ∈ Γ}), where LC M is lowest common multiple. The energy consumption of core k during one interval L can be expressed as follows [15], [16]:…”

Section: System Modelsmentioning

confidence: 99%

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Energy-Aware Task Allocation for Heterogeneous Multiprocessor Systems by Using Integer Linear Programming

Qin

Zeng

Kurachi

et al. 2019

Journal of Information Processing

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Energy-aware task allocation of embedded systems is one of the most important issues in recent decades. A classical solution to solve the issue is Integer Linear Programming (ILP). However, given the considerable time consumption, it is effective only to the extent that the scale of the problem is small. How to use ILP to solve large allocation problems on heterogeneous multiprocessor systems to minimize energy consumption is still a challenge. This paper proposes two ILP formulations to deal with it. One complete ILP( 1) is used to derive a feasible allocation, and the other simplified ILP(2) is for calculating the desired minimum energy. Then the desired minimum energy can be used as a reference to evaluate the intermediate solution of ILP(1) and decide its timeout. Besides, to find out the bestsuited platform for a given workload, a flexible design which presents flexibilities and choices in core assignment, is considered for further energy saving. For example, the optimal core number design and core type design are generated as two independent ILP formulations, denoted as ILP(3) and ILP(4). The experimental results on randomly generated task sets demonstrate that, compared with the fixed platform, automatically synthesizing a flexible core assignment saves more energy.

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Section: Real-time Scheduling On a Fixed Platformmentioning

confidence: 99%

Section: System Modelsmentioning

confidence: 99%

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Energy-Aware Task Allocation for Heterogeneous Multiprocessor Systems by Using Integer Linear Programming

Qin

Zeng

Kurachi

et al. 2019

Journal of Information Processing

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“…The research on reducing the energy consumption of heterogeneous multi-cores in embedded systems has made considerable progress. The application of DVFS and DPM lowpower technology further reduces the energy consumption of heterogeneous multi-core embedded systems [6], [13]. The DVFS technology provides several discrete voltage levels, and the processor can independently and dynamically adjust its own voltage supply to save energy [14], [15], [16].…”

Section: Related Workmentioning

confidence: 99%

Energy-aware Task Scheduling Strategies for Multi-core Embeddded Systems

Liu¹,

Liu²

2018

EasyChair Preprints

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In this paper, we propose two energy-aware scheduling algorithms---(1) Reinforcement learning-based multiprocessor scheduling (RL) algorithm and (2) Mathematical morphology multiprocessor scheduling (MMS) algorithm---for scheduling time-constrained Directed Acyclic Graph (DAG) tasks in an embedded multiprocessor system with Dynamic Voltage And Frequency Scaling (DVFS) and Dynamic Power Management (DPM) technology. Unlike other heuristic scheduling algorithms, the proposed reinforcement learning (RL) is a machine learning algorithm, rarely considered for energy-aware scheduling in DAG tasks. The MMS, inspired by Mathematical morphology that is often used in image processing, continuously adjusts the coded scheduling through a probe matrix to optimize energy consumption. In this paper the genetic algorithm (GA) is compared with these two proposed algorithms by rigorous simulation. The results demonstrate that our algorithms are more energy efficient. Compared with the GA algorithm, the RL and the MMS algorithm significantly improve the energy consumption reduction rate by an average of 13.37% and 72.92% respectively. In addition, MMS algorithm shows better performance in high-density and high-complexity DAG tasks.

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“…Hence, whenever evaluating another core migration, one can estimate the corresponding c j value in the target core. Since at application start-up no prior information is available, an initial value of β A7→A15 = 2 is set in the considered Exynous 5410 SoC, which is defined as a reasonable estimate according to our experimental validation and is supported by Colin et al [2015] and , where the actual value is shown to vary between 1 and 3, depending on the application characteristics. It should, however, be noted that, although a lookup table could be used to preinitialize the values according to the application characteristics (as in Cochran et al [2011]), adaptive modeling is used to determine the exact value once a new beep is generated on the target core, in order to avoid errors due to phase changes.…”

Section: Task Performance Modelingmentioning

confidence: 99%

A Framework for Application-Guided Task Management on Heterogeneous Embedded Systems

Gaspar

Taniça

Pfister

et al. 2015

ACM Trans. Archit. Code Optim.

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In this article, we propose a general framework for fine-grain application-aware task management in heterogeneous embedded platforms, which allows integration of different mechanisms for an efficient resource utilization, frequency scaling, and task migration. The proposed framework incorporates several components for accurate runtime monitoring by relying on the OS facilities and performance self-reporting for parallel and iterative applications. The framework efficiency is experimentally evaluated on a real hardware platform, where significant power and energy savings are attained for SPEC CPU2006 and PARSEC benchmarks, by guiding frequency scaling and intercluster migrations according to the runtime application behavior and predefined performance targets. CCS Concepts: r Computer systems organization → Multicore architectures; Heterogeneous (hybrid) systems; r Software and its engineering → Process management;

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Energy-Efficient Allocation of Real-Time Applications onto Single-ISA Heterogeneous Multi-Core Processors

Cited by 28 publications

References 21 publications

Energy-Aware Task Allocation for Heterogeneous Multiprocessor Systems by Using Integer Linear Programming

Energy-Aware Task Allocation for Heterogeneous Multiprocessor Systems by Using Integer Linear Programming

Energy-aware Task Scheduling Strategies for Multi-core Embeddded Systems

A Framework for Application-Guided Task Management on Heterogeneous Embedded Systems

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