Load-prediction scheduling algorithm for computer simulation of electrocardiogram in hybrid environments

Shen, Wenfeng; Luo, Zhaokai; Wei, Daming; Xu, Weimin; Zhu, Xin

doi:10.1016/j.jss.2015.01.015

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…According to Automotive Open System Architecture (AUTOSAR) 4.0, there are certain limitations on multicore software implementation. The scheduling algorithms strictly partition the tasks based on similar functionality, periodicity or dependency and assign those tasks to fixed cores [10,11]. Tasks are scheduled in the intended core based on their statically assigned priority.…”

Section: Scheduling Challenges On Multicore Ecusmentioning

confidence: 99%

“…Tasks are scheduled in the intended core based on their statically assigned priority. In the current scenario, tricore microcontroller implemented in the multicore ECU is utilized upto 60% in normal running condition and few cores are even less utilized [11]. When load increases with the speed of the vehicle, it is transferred to under loaded cores.…”

Section: Scheduling Challenges On Multicore Ecusmentioning

confidence: 99%

See 1 more Smart Citation

Performance optimization of task intensive real‐time applications on multicore ECUs—A hybrid scheduler

Mishra¹,

Hegde²

2021

The Journal of Engineering

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In the current approach of automotive electronic system design, the multicore processors have prevailed to achieve high computing performance at low thermal dissipation. Multicore processors offer functional parallelism that helps in meeting the safety critical requirements of vehicles. The number of Electronic Control Units (ECUs) in high-end cars could be reduced by conglomerating more functions into a multicore ECU. AUTOSAR stack has been designed to support the applications developed for multicore ECUs. The real challenges lie in adapting new design methods while developing sophisticated applications with multicore constraints. It is imperative to utilize the most of multicore computational capability towards enhancing the overall performance of ECUs. In this context, the scheduling of the real-time multitasking software components by the operating system is one of the challenging issues to be addressed. Here, the state-of-the-art scheduling algorithm is reviewed and its merits and limitations are identified. A hybrid scheduler has been proposed, tested and compared with the state-of-the-art algorithm that offers better performance in terms of CPU utilization, average response time and deadline missing rate both in normal and high load conditions.

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Section: Scheduling Challenges On Multicore Ecusmentioning

confidence: 99%

Section: Scheduling Challenges On Multicore Ecusmentioning

confidence: 99%

Performance optimization of task intensive real‐time applications on multicore ECUs—A hybrid scheduler

Mishra¹,

Hegde²

2021

The Journal of Engineering

View full text Add to dashboard Cite

show abstract

“…According to Automotive Open System Architecture (AUTOSAR) 4.0, there are certain limitations on multicore software implementation. The scheduling algorithms strictly partition the tasks based on similar functionality or periodicity or dependency and assign those tasks to fixed cores [10,11]. Tasks are scheduled in the intended core based on their statically assigned priority.…”

Section: Scheduling Challenges On Multicore Ecusmentioning

confidence: 99%

Performance optimization of task intensive real time applications on multicore ECUs - a hybrid scheduler

Mishra

Hegde

2019

IJRES

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In the current approach of Automotive electronic system design, the multicore processors have prevailed to achieve high computing performance at low thermal dissipation. Multicore processors offer functional parallelism that helps in meeting the safety critical requirements of vehicles. The number of ECUs in high-end cars could be reduced by conglomerating more functions into a multicore ECU. AUTOSAR stack has been designed to support the applications developed for multicore ECUs. The real challenges lie in adapting new design methods while developing sophisticated applications with multicore constraints. It is imperative to utilize the most of multicore computational capability towards enhancing the overall performance of ECUs. In this context the scheduling of the real time multitasking software components by the operating system is one of the key issues to be addressed. In this paper, the state of the art scheduling algorithm is reviewed and its merits and limitations are identified. A hybrid scheduler has been proposed, tested and compared with the state of the art algorithm that offers better performance in terms of CPU utilization, average response time and deadline missing rate both in normal and high load conditions.

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“…Heterogeneous computing [18] is a computing paradigm that tries to jointly exploit different kind of computational units, not only the traditional CPU cores, but also graphical accelerators (GPUs) [166], multi-core chip coprocessor (XeonPhi) [167], or customized integrated circuits (FPGAs) [168], among others. During the last decade, the interest of the scientific community has significantly raised for these heterogeneous environments, although their programming is a tedious task that has a long learning curve.…”

Section: State Of the Art: Towards Heterogeneous Computingmentioning

confidence: 99%

Parallel approaches to shortest-path problems for multilevel heterogeneous computing

Arranz¹

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Modelo paralelo abstracto, APSP, tuning automático de kernels, Boost Graph Library, configuración de la Cache L1, ejecución concurrente de kernels, CUDA, Dijkstra, GPGPU, sistemas heterogéneos, framework HPC, modelo de caracterización de kernels, proceso de caracterización de kernels, balanceo de carga, MPI, comparativa de plataformas de NVIDIA, OpenMP, técnicas de optimización, algoritmos paralelos, SSSP, geometría del bloque de hilos.

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Load-prediction scheduling algorithm for computer simulation of electrocardiogram in hybrid environments

Cited by 6 publications

References 18 publications

Performance optimization of task intensive real‐time applications on multicore ECUs—A hybrid scheduler

Performance optimization of task intensive real‐time applications on multicore ECUs—A hybrid scheduler

Performance optimization of task intensive real time applications on multicore ECUs - a hybrid scheduler

Parallel approaches to shortest-path problems for multilevel heterogeneous computing

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