Load Balancing Aware Real-Time Task Partitioning in Multicore Systems

2012 ◽  
Author(s):  
Jaeyeon Kang ◽  
Daniel G. Waddington
Keyword(s):  
Load Balancing ◽  
Real Time ◽  
Multicore Systems ◽  
Task Partitioning ◽  
Time Task

Harmonicity-Aware Task Partitioning for Fixed Priority Scheduling of Probabilistic Real-Time Tasks on Multi-Core Platforms

2017 ◽  
Vol 16 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Tianyi Wang ◽  
Soamar Homsi ◽  
Linwei Niu ◽  
Shaolei Ren ◽  
Ou Bai ◽  
...  
Keyword(s):  
Real Time ◽  
Task Partitioning ◽  
Priority Scheduling ◽  
Fixed Priority ◽  
Fixed Priority Scheduling

Prepare: P owe r -Awar e A p proximate Re a l-time Task Scheduling for Ene r gy-Adaptiv e QoS Maximization

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Shounak Chakraborty ◽  
Sangeet Saha ◽  
Magnus Själander ◽  
Klaus Mcdonald-Maier
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Processing Speed ◽  
Empirical Evaluation ◽  
Adaptive Mechanism ◽  
Approximate Computing ◽  
Thermal Safety ◽  
Time Task ◽  
Individual Task ◽  
The Individual

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose Prepare , a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of Prepare reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks. Prepare exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload, Prepare offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multi-processor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety, Prepare reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of Prepare outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.

scholarly journals Contention & Energy-Aware Real-Time Task Mapping on NoC Based Heterogeneous MPSoCs

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 75110-75123 ◽  
Author(s):  
Haider Ali ◽  
Umair Ullah Tariq ◽  
Yongjun Zheng ◽  
Xiaojun Zhai ◽  
Lu Liu
Keyword(s):  
Real Time ◽  
Task Mapping ◽  
Energy Aware ◽  
Time Task
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