Real-Time scheduling and analysis of parallel tasks on heterogeneous multi-cores

Chang, Shuangshuang; Zhao, Xufeng; Liu, Zhenyu; Deng, Qingxu

doi:10.1016/j.sysarc.2019.101704

Cited by 32 publications

(14 citation statements)

References 29 publications

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“…In conclusion, we demonstrate that the partition scheduling algorithm scheme developed by Chang [3] is the most suitable algorithm for heterogeneous multicore mobile systems for scheduling multimedia tasks such as coding and decoding. With the expected rise in smartphone usage and with increased multimedia capabilities being added every year there is a need for fast paced and optimized schedulers and our implementation is a step in that direction to solve the problem.…”

Section: Jpeg Codecmentioning

confidence: 79%

“…After reviewing the available literature, we decided to model some commonly used codec algorithms as task graphs and implement the scheduling algorithm provided in [3] in the Python programming language. To test the results of our implementation, we are using the H.263 codec, JPEG codec and MP3 codec.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, in Chang et al [3], the authors have taken into account the arguments made regarding [2] and improved the scheduling bounds for heterogeneous multi core systems. It is our wish now to use these three.…”

Section: Literature Surveymentioning

confidence: 99%

“…Finally, Chang's worst case response time [3] is theoretically calculated based on the formula given in Equation 3.…”

Section: R(g) ≤ Len(g) + σS ϵ S (Vols(g)/ms) -Len(g)/maxs ϵ S{ms} (1)mentioning

confidence: 99%

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Comparative Study of Heterogeneous Multicore Scheduling Algorithms on Media Codecs

Jamadagni¹,

Aniruddh²,

M³

et al. 2021

JUSST

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All modern-day computers and smartphones come with multi-core CPUs. The multicore architecture is generally heterogeneous in nature to maximize computational throughput. These multicore systems exploit thread-level parallelism to deliver higher performance, but they are limited by the requirement of good scheduling algorithms that maximize CPU utility and minimize wasted and idle cycles. With the rise in streaming services and multimedia capabilities of smartphones, it is necessary to have efficient heterogeneous cores which are capable of performing multimedia processing at a fast pace. It is also needed that they utilize efficient scheduling algorithms to achieve this task. This paper compares some heterogeneous multi-core scheduling algorithms available and determines which is the most optimal scheduling algorithm given various codecs.

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Section: Jpeg Codecmentioning

confidence: 79%

Section: Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

“…Finally, Chang's worst case response time [3] is theoretically calculated based on the formula given in Equation 3.…”

Section: R(g) ≤ Len(g) + σS ϵ S (Vols(g)/ms) -Len(g)/maxs ϵ S{ms} (1)mentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Heterogeneous Multicore Scheduling Algorithms on Media Codecs

Jamadagni¹,

Aniruddh²,

M³

et al. 2021

JUSST

View full text Add to dashboard Cite

show abstract

“…For homogeneous multiprocessors with a global scheme, existing scheduling (and their analysing) methods aim at reducing the makespan and tightening the worst-case analytical bound. They can be classified as either slice-based [18], [19] or node-based [1], [20]. The slice-based schedule enforces node-level preemption and divides each node into a number of small computation units (e.g., units with a WCET of one in Chang [18]).…”

Section: B Scheduling a Single Dag Taskmentioning

confidence: 99%

DAG Scheduling and Analysis on Multi-Core Systems by Modelling Parallelism and Dependency

Zhao

Dai

Bate

2022

IEEE Trans. Parallel Distrib. Syst.

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With ever more complex functionalities being implemented in emerging real-time applications, multi-core systems are demanded for high performance, with directed acyclic graphs (DAG) being used to model functional dependencies. For a single DAG task, our previous work presented a concurrent provider and consumer (CPC) model that captures the node-level dependency and parallelism, which are the two key factors of a DAG. Based on the CPC, scheduling and analysis methods were constructed to reduce makespan and tighten the analytical bound of the task. However, the CPC-based methods cannot support multi-DAGs as the interference between DAGs (i.e., inter-task interference) is not taken into account. To address this limitation, this paper proposes a novel multi-DAG scheduling approach which specifies the number of cores a DAG can utilise so that it does not incur the inter-task interference. This is achieved by modelling and understanding the workload distribution of the DAG and the system. By avoiding the inter-task interference, the constructed schedule provides full compatibility for the CPC-based methods to be applied on each DAG and reduces the pessimism of the existing analysis. Experimental results show that the proposed multi-DAG method achieves an improvement up to 80% in schedulability against the original work that it extends, and outperforms the existing multi-DAG methods by up to 60% for tightening the interference.

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Evolvement of Scheduling Theories for Autonomous Vehicles

Chang,

Chen,

Zhao

et al. 2023

Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems

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Real-Time scheduling and analysis of parallel tasks on heterogeneous multi-cores

Cited by 32 publications

References 29 publications

Comparative Study of Heterogeneous Multicore Scheduling Algorithms on Media Codecs

Comparative Study of Heterogeneous Multicore Scheduling Algorithms on Media Codecs

DAG Scheduling and Analysis on Multi-Core Systems by Modelling Parallelism and Dependency

Evolvement of Scheduling Theories for Autonomous Vehicles

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