Peak power reduction methodology for multi-core systems

Lee, Bongki; Kim, Jaehwan; Jeung, Yeun-Cheul; Chong, Jong‐Wha

doi:10.1109/socdc.2010.5682930

Cited by 19 publications

(11 citation statements)

References 4 publications

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“…Some related works focused on minimizing the peak power consumption under real-time constraints [1][13] [64]. Lee et al [1] have proposed a new scheduling algorithm for realtime tasks to minimize chip-level power consumption, without relying on any extra hardware (e.g., DVFS controller).…”

Section: B Peak-power Managementmentioning

confidence: 99%

“…This work restricts the concurrent execution of tasks that are assigned to different cores. Lee et al [64] has proposed a task scheduling that prevents the occurrence of the peak power consumption for task-graph models. The proposed algorithm in [64] schedules the tasks, considering data dependency information while reducing the peak power.…”

Section: B Peak-power Managementmentioning

confidence: 99%

“…Lee et al [64] has proposed a task scheduling that prevents the occurrence of the peak power consumption for task-graph models. The proposed algorithm in [64] schedules the tasks, considering data dependency information while reducing the peak power. As one of the most related work, Munawar et al [13] have presented a scheme to minimize the peak power for frame-based and periodic tasks with real-time constraints on multicore systems.…”

Section: B Peak-power Managementmentioning

confidence: 99%

See 2 more Smart Citations

Peak-Power-Aware Primary-Backup Technique for Efficient Fault-Tolerance in Multicore Embedded Systems

et al. 2020

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Section: B Peak-power Managementmentioning

confidence: 99%

Section: B Peak-power Managementmentioning

confidence: 99%

Section: B Peak-power Managementmentioning

confidence: 99%

See 1 more Smart Citation

Peak-Power-Aware Primary-Backup Technique for Efficient Fault-Tolerance in Multicore Embedded Systems

et al. 2020

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“…The problem is important in both embedded [16][17][18] as well as super-computing domain [19,20]. The authors in [21] propose an algorithm to minimize peak power for an application with a task graph without a deadline. The authors of [22] were the first to study the problem of peak power minimization in the context of task graph-based many-core applications with a deadline.…”

Section: Related Workmentioning

confidence: 99%

PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications

Maity

Pathania

Mitra

2020

JLPEA

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Multiple multi-threaded tasks constitute a modern many-core application. An accompanying generic Directed Acyclic Graph (DAG) represents the execution precedence relationship between the tasks. The application comes with a hard deadline and high peak power consumption. Parallel execution of multiple tasks on multiple cores results in a quicker execution, but higher peak power. Peak power single-handedly determines the involved cooling costs in many-cores, while its violations could induce performance-crippling execution uncertainties. Less task parallelization, on the other hand, results in lower peak power, but a more prolonged deadline violating execution. The problem of peak power minimization in many-cores is to determine task-to-core mapping configuration in the spatio-temporal domain that minimizes the peak power consumption of an application, but ensures application still meets the deadline. All previous works on peak power minimization for many-core applications (with or without DAG) assume only single-threaded tasks. We are the first to propose a framework, called PkMin, which minimizes the peak power of many-core applications with DAG that have multi-threaded tasks. PkMin leverages the inherent convexity in the execution characteristics of multi-threaded tasks to find a configuration that satisfies the deadline, as well as minimizes peak power. Evaluation on hundreds of applications shows PkMin on average results in 49.2% lower peak power than a similar state-of-the-art framework.

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“…In this situation, the execution of all LC and HC tasks requires higher computational demands, which may exceed the processor's capacity, and the system becomes overloaded [6]. Thus, all cores may execute tasks simultaneously to meet deadlines of tasks, which increase the instantaneous processor power beyond its Thermal Design Power (TDP) constraint [7]- [9]. TDP is the maximum sustainable power that a chip can dissipate safely.…”

Section: Introductionmentioning

confidence: 99%

Toward the Design of Fault-Tolerance-Aware and Peak-Power-Aware Multicore Mixed-Criticality Systems

Ranjbar

Hosseinghorban

Salehi

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Mixed-Criticality (MC) systems have recently been devised to address the requirements of real-time systems in industrial applications, where the system runs tasks with different criticality levels on a single platform. In some workloads, a highcritically task might overrun and overload the system, or a fault can occur during the execution. However, these systems must be fault-tolerant and guarantee the correct execution of all highcriticality tasks by their deadlines to avoid catastrophic consequences, in any situation. Furthermore, in these MC systems, the peak power consumption of the system may increase, especially in an overload situation and exceed the processor Thermal Design Power (TDP) constraint. This may cause generating heat beyond the cooling capacity, resulting the system stop to avoid excessive heat and halting the processor. In this paper, we propose a technique for dependent dual-criticality tasks in fault-tolerant multi-core MC systems to manage peak power consumption and temperature. The technique develops a tree of possible task mapping and scheduling at design-time to cover all possible scenarios and reduce the low-criticality task drop rate in the highcriticality mode. At run-time, the system exploits the tree to select a proper schedule according to fault occurrences and criticality mode changes. Experimental results show that the average task schedulability is 74.14% on average for the proposed method, while the peak power consumption and maximum temperature are improved by 16.65% and 14.9 • C on average, respectively, compared to a recent work. In addition, for a real-life application, our method reduces the peak power and maximum temperature by up to 20.06% and 5 • C, respectively, compared to a state-ofthe-art approach.

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Peak power reduction methodology for multi-core systems

Cited by 19 publications

References 4 publications

Peak-Power-Aware Primary-Backup Technique for Efficient Fault-Tolerance in Multicore Embedded Systems

Peak-Power-Aware Primary-Backup Technique for Efficient Fault-Tolerance in Multicore Embedded Systems

PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications

Toward the Design of Fault-Tolerance-Aware and Peak-Power-Aware Multicore Mixed-Criticality Systems

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