Fast and efficient voltage scheduling by evolutionary slack distribution

Gorjiara, Bita; Chou, Pai H.; Bagherzadeh, Nader; Reshadi, Midia; Jensen, David L.

doi:10.1109/aspdac.2004.1337674

Cited by 11 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Implementing the parallelizing ACO-VTS and its approximation approach by C++ with MPI, we compare the results of the two algorithms operated on the Blade Server with EE-GLSA and ASG-VTS based on benchmarks tgff1-tgff25 used in [1,2,4,5]. These benchmarks are generated using TGFF [8], a tool producing pseudo-random task graphs, so it is quite practical in the real world.…”

Section: Resultsmentioning

confidence: 99%

A Novel Voltage Scaling Algorithm through Ant Colony Optimization for Embedded Distributed Systems

Ding

Zhang

Zheng

2007

2007 IEEE International Conference on Integration Technology

View full text Add to dashboard Cite

Dynamic Voltage Scaling, supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results. However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. This paper presents a new algorithm based on ant colony optimization, called Ant Colony Optimization Voltage and Task Scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algorithm. Both of them generate quite good results, saving up to 30% more energy than one of the previous ones under coarse-grained modes, but their results don't depend on the number of modes available.

show abstract

Section: Resultsmentioning

confidence: 99%

A Novel Voltage Scaling Algorithm through Ant Colony Optimization for Embedded Distributed Systems

Ding

Zhang

Zheng

2007

2007 IEEE International Conference on Integration Technology

View full text Add to dashboard Cite

show abstract

“…An evolutionary scheme to schedule DAGs on multiprocessors for minimizing the task completion time has been proposed in [6]. A fast and efficient task scheduling and voltage selection approach called Evolutionary Relative Slack Distribution Voltage Scheduling (ERSD-VS) has been introduced in [9].…”

Section: Related Workmentioning

confidence: 99%

Simultaneous optimization of performance, energy and temperature for DAG scheduling in multi-core processors

Sheikh

Ahmad

2012

2012 International Green Computing Conference (IGCC)

View full text Add to dashboard Cite

This paper addresses the joint optimization of performance, energy, and temperature, termed as PEToptimization. This multi-objective PET-optimization is achieved in scheduling DAGs on multi-core systems. Our technique is based on multi-objective evolutionary algorithm (MOEA) for finding Pareto optimal solutions using scheduling and voltage selection. These solutions are not necessarily scalar values but can be in a vector form. We developed a Strength Pareto Evolutionary Algorithm [2] (SPEA) based solution which is inherently superior to several other MOEA methods. The proposed algorithm obtains the Pareto vectors (or fronts) efficiently. The work is novel and original in the sense that no previous such optimization work has been reported to our knowledge for the PET-optimization scheduling problem. The strength of the proposed algorithm is that it achieves diverse range of energy and thermal improvements while staying close to the performance-optimal point to ensure efficient trade-off solutions. The proposed approach consists of two-steps. In the first step, Pareto fronts are generated. In the second step, one most optimal solution is selected. Simulation results on several benchmark task graph applications demonstrate that efficient solutions can be selected using the proposed selection method in polynomial time.

show abstract

“…To further reduce the complexity of slack distribution, we introduce the notion of time-based relatives of a task (Section 4.2) as a heuristic that helps finding the candidates for slowdown and speedup quickly and efficiently. We compare our algorithm with two of the most energy efficient approaches: RVS [19] and EE-GLSA [15]. Experimental results from running publicly available tight-deadline benchmarks show that with only four modes to choose from, ASG-VTS can save up to 26% and 33% more energy compared to RVS and EE-GLSA.…”

Section: 2mentioning

confidence: 99%

“…Since the ILP has a very long runtime, they have used a partitioning heuristic to compromise the optimality with the speed. In [19], a voltage selection technique (RVS) is proposed that randomly distributes the slack time among the high-power tasks over several iterations. It adjusts the ordering of tasks to make the selected modes schedulable.…”

Section: Related Workmentioning

confidence: 99%

An efficient voltage scaling algorithm for complex SoCs with few number of voltage modes

Gorjiara

Bagherzadeh

Chou

2004

Proceedings of the 2004 International Symposium on Low Power Electronics and Design

View full text Add to dashboard Cite

Increasing demand for larger high-performance applications requires developing more complex systems with hundreds of processing cores on a single chip. To allow dynamic voltage scaling in each on-chip cores individually, many on-chip voltage regulators must be used. However, the limitations in implementation of onchip inductors can reduce the efficiency, accuracy and the number of voltage modes generated by regulators. Therefore the future voltage scheduling algorithms must be efficient, even in the presence of few voltage modes; and fast, in order to handle complex applications. Techniques proposed to date, need many fine-grained voltage modes to produce energy efficient results and their quality degrades significantly as the number of modes decreases. This paper presents a new technique called Adaptive Stochastic Gradient Voltage and Task Scheduling (ASG-VTS) that quickly generates very energy efficient results irrespective of the number of available voltage modes. The results of comparing our algorithm to the most efficient approaches (RVS and EE-GLSA) show that in the presence of only four valid modes, the ASG-VTS saves up to 26% and 33% more energy. On the other hand, other approaches require at least ten modes to reach the same level of energy saving that ASG-VTS achieves with only four modes. Therefore our algorithm can also be used to explore and minimize the number of required voltage levels in the system. Categories and Subject Descriptors

show abstract

Fast and efficient voltage scheduling by evolutionary slack distribution

Abstract: Abstract

Cited by 11 publications

References 9 publications

A Novel Voltage Scaling Algorithm through Ant Colony Optimization for Embedded Distributed Systems

A Novel Voltage Scaling Algorithm through Ant Colony Optimization for Embedded Distributed Systems

Simultaneous optimization of performance, energy and temperature for DAG scheduling in multi-core processors

An efficient voltage scaling algorithm for complex SoCs with few number of voltage modes

Contact Info

Product

Resources

About