Dynamic core allocation for energy efficient video decoding in homogeneous and heterogeneous multicore architectures

Pal, Rajesh Kumar; Shanaya, Ierum; Paul, Kolin; Prasad, Sanjiva

doi:10.1016/j.future.2015.09.018

Cited by 9 publications

(10 citation statements)

References 4 publications

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“…In §2.1, we compare the capabilities of different resource configurations in executing a specified workload, with Propositions 2-5 providing some useful properties. The framework is extended to deal with reconfiguration ( §2.2), following which we show the correctness of the scheme proposed by Pal et al in [20] (Theorem 1). The weighted transition systems are extended to account for energy consumption in §3, using which we are able to formally state that the scheme of Pal et al performs better than the baseline configuration (Theorem 2).…”

Section: Introductionmentioning

confidence: 84%

“…The scheme proposed by Pal et al, [20], permits reconfiguration from r to a weaker configuration r only when sufficient slack has been earned to permit a slower execution of the next action plus time for reconfiguration (before and possibly after), i.e., r,t i−1 τ(r , a i )). That is, the sum of the slack earned so far and the budgeted time for a i should exceed the time for reconfiguring and executing on a slower configuration, with a further allowance for a possible reconfiguration to a faster configuration to avoid missing future deadlines.…”

Section: Reconfigurationmentioning

confidence: 99%

“…where t i = t i−1 + δ + τ(r , a i ), and e i = e i−1 + e, where e = θ r,r + γ(r , a i ). [20] is a better (more efficient) by-simulation than execution on the baseline configuration r max .…”

Section: Better: Comparing Resources Based On Energy Efficiencymentioning

confidence: 99%

“…The scheme in [20] is not optimal for arbitrary workloads. For finite workloads it is possible to determine optimal executions using offline techniques such as the YDS algorithm [22], or dynamic programming techniques for related problems.…”

Section: What's Best?mentioning

confidence: 99%

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Best-by-Simulations: A Framework for Comparing Efficiency of Reconfigurable Multicore Architectures on Workloads with Deadlines

Prasad

2017

Electron. Proc. Theor. Comput. Sci.

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Energy consumption is a major concern in multicore systems. Perhaps the simplest strategy for reducing energy costs is to use only as many cores as necessary while still being able to deliver a desired quality of service. Motivated by earlier work on a dynamic (heterogeneous) core allocation scheme for H.264 video decoding that reduces energy costs while delivering desired frame rates, we formulate operationally the general problem of executing a sequence of actions on a reconfigurable machine while meeting a corresponding sequence of absolute deadlines, with the objective of reducing cost. Using a transition system framework that associates costs (e.g., time, energy) with executing an action on a particular resource configuration, we use the notion of amortised cost to formulate in terms of simulation relations appropriate notions for comparing deadline-conformant executions. We believe these notions can provide the basis for an operational theory of optimal cost executions and performance guarantees for approximate solutions, in particular relating the notion of simulation from transition systems to that of competitive analysis used for, e.g., online algorithms.

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Section: Introductionmentioning

confidence: 84%

Section: Reconfigurationmentioning

confidence: 99%

Section: Better: Comparing Resources Based On Energy Efficiencymentioning

confidence: 99%

Section: What's Best?mentioning

confidence: 99%

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Best-by-Simulations: A Framework for Comparing Efficiency of Reconfigurable Multicore Architectures on Workloads with Deadlines

Prasad

2017

Electron. Proc. Theor. Comput. Sci.

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“…As the hardware processors can be designed to support DVF-S, there are energy efficiency optimizations on dynamic voltage scheduling and multi-core CPUs [34][35][36]. Wu et al modeled the dynamic power as a convex function of the processor speed, and proposed an blocking-aware method [34] to dynamically schedule real-time tasks and adjust the processor speed.…”

Section: Related Workmentioning

confidence: 99%

Bandwidth-aware energy efficient flow scheduling with SDN in data center networks

Guan

Dai

Huang

et al. 2017

Future Generation Computer Systems

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Nowadays the energy consumption has become one of the most urgent issues for Data center networks. For general network devices, the power is constant and independent from the actual transfer rate. Therefore the network devices are energy efficient when they are in full workload. The flow scheduling methods based on the exclusive routing can reduce the network energy consumption, as the exclusive routing paths can fully utilize all their links. However, these methods will no longer guarantee the energy efficiency of switches, as they handle flows in priority order by greedily choosing the path of available links instantaneously. In a previous work we proposed an extreme case of flow scheduling based on both link and switch utilization. Herein we consider general scenarios in data center networks and propose a novel energy efficient flow scheduling and routing algorithm in SDN. This method minimizes the overall energy for data center traffic in time dimension, and increases the utilization of switches and meet the flow requirements such as deadline. We did a series of simulation studies in the INET framework of OMNet++. The experiment results show that our algorithm can reduce the overall energy with respect to the traffic volume and reduce the flow completion time on average.

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Special Issue on: Multicore and Many-core Architectures for Future Generation Embedded Systems

Hassan

Yang

Zhang

et al. 2016

Future Generation Computer Systems

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Dynamic core allocation for energy efficient video decoding in homogeneous and heterogeneous multicore architectures

Cited by 9 publications

References 4 publications

Best-by-Simulations: A Framework for Comparing Efficiency of Reconfigurable Multicore Architectures on Workloads with Deadlines

Best-by-Simulations: A Framework for Comparing Efficiency of Reconfigurable Multicore Architectures on Workloads with Deadlines

Bandwidth-aware energy efficient flow scheduling with SDN in data center networks

Special Issue on: Multicore and Many-core Architectures for Future Generation Embedded Systems

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