H-EARtH: Heterogeneous Multicore Platform Energy Management

Rotem, Efraim; Weiser, Uri; Mendelson, Avi; Ginosar, Ran; Weissmann, Eliezer; Aizik, Yoni

doi:10.1109/mc.2016.309

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…With regard to the intra-task DVFS approach, the slack time is reduced in different execution paths, and as a result, more energy saving is expected from the scheduling. Moreover, the study of task partitioning on a real-time embedded system was related to hardware configurations, e.g., memory management, cache size, and architecture design [21]- [24]. In this work, we aim at exploring energy-efficient scheduling with respect to an intra-task technique in a uniprocessor environment.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Intra-task DVFS Scheduling Using Linear Programming Formulation

et al. 2019

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In real-time embedded systems, minimizing energy consumption is one of the most important tasks. Intra-task dynamic voltage and frequency scaling (DVFS) has been the subject of much research in the task boundary of time-constrained applications for energy reduction. The problem of optimizing energy consumption with respect to intra-task DVFS scheduling can be addressed by assigning proper operational frequencies to individual basic blocks in a program while guaranteeing the deadline. Based on the profile information of a task, we first formulate the problem in terms of integer linear programming (ILP) regarding different assumptions of transition overhead. To verify the effectiveness of ILP formulations, the most representative intra-task DVFS techniques are taken for comparisons. The results of the experiments demonstrate that the proposed ILP method achieves greater energy savings than the existing approaches. Moreover, it determines the optimal scheduling strategy in reasonable execution time for applications with a limited number of blocks. INDEX TERMS Intra-task DVFS technique, time constrained applications, minimize energy consumption, ILP formulations.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Intra-task DVFS Scheduling Using Linear Programming Formulation

et al. 2019

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“…The Heterogeneous Energy‐Aware Race to Halt (H‐EARtH) algorithm described in the work of Rotem et al considers the availability of heterogeneous platforms and DVFS capabilities, to provide a runtime procedure that determines the best core and its corresponding voltage and frequency values to optimize energy consumption. In general, the previously cited papers on energy‐aware scheduling procedures show relevant improvements in energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU‐GPU architectures

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et al. 2018

Concurrency and Computation

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Summary By means of the availability of mechanisms such as Dynamic Voltage and Frequency Scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles, it is possible to devise scheduling algorithms that are aware of both runtime and energy consumption in parallel programs. In this paper, we propose and evaluate a multi‐objective (more specifically, a bi‐objective) approach to distribute the workload among the processing cores in a given heterogeneous parallel CPU‐GPU architecture. The aim of this distribution may be either to save energy without increasing the running time or to reach a trade‐off among time and energy consumption. The parallel programs considered here are master‐worker evolutionary algorithms where the evaluation of the fitness function for the individuals in the population demands the most part of the computing time. As many useful bioinformatics and data mining applications exhibit this kind of parallel profile, the proposed energy‐aware approach for workload scheduling could be frequently applied.

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“…The main approaches to the development of energy-efficient parallel and distributed codes can be grouped into two alternatives. Several approaches propose scheduling procedures that take into account not only running time but also energy consumption of the program (Baskiyar and Abdel-Kader, 2010;Dorronsoro et al, 2014;Lee and Zomaya, 2011;Nesmachnow et al, 2013;Rotem et al, 2016;Zhang et al, 2002). Other approaches investigate the effect of different implementations for a specific application in energy consumption and try to derive energy-aware strategies and power models from the corresponding experimental results (Aliaga et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

A Power–Performance Perspective to Multiobjective Electroencephalogram Feature Selection on Heterogeneous Parallel Platforms

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et al. 2018

Journal of Computational Biology

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This article provides an insight on the power-performance issues related with the CPU-GPU (Central Processing Unit-Graphics Processing Unit) parallel implementations of problems that frequently appear in the context of applications on bioinformatics and biomedical engineering. More specifically, we analyze the power-performance behavior of an evolutionary parallel multiobjective electroencephalogram feature selection procedure that evolves subpopulations of solutions with time-demanding fitness evaluation. The procedure has been implemented in OpenMP to dynamically distribute either subpopulations or individuals among devices, and uses OpenCL to evaluate the fitness of the individuals. The development of parallel codes usually implies to maximize the code efficiency, thus optimizing the achieved speedups. To follow the same trend, this article extends and provides a more complete analysis of our previous works about the power-performance characteristics in heterogeneous CPU-GPU platforms considering different operation frequencies and evolutionary parameters, such as distribution of individuals, etc. This way, different experimental configurations of the proposed procedure have been evaluated and compared with respect to a master-worker approach, not only in runtime but also considering energy consumption. The experimental results show that lower operating frequencies does not necessarily mean lower energy consumptions since energy is the product of power and time. Thus, we have observed that parallel processing not only reduces the runtime, but also the energy consumed by the application despite a higher instantaneous power. Particularly, the workload distribution among both CPU and GPU cores provides the best runtime and very low energy consumption compared with the values achieved by the same alternatives executed by only CPU threads.

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H-EARtH: Heterogeneous Multicore Platform Energy Management

Cited by 14 publications

References 9 publications

Energy-Efficient Intra-task DVFS Scheduling Using Linear Programming Formulation

Energy-Efficient Intra-task DVFS Scheduling Using Linear Programming Formulation

Energy‐aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU‐GPU architectures

A Power–Performance Perspective to Multiobjective Electroencephalogram Feature Selection on Heterogeneous Parallel Platforms

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