A solution to drawbacks in capturing execution requirements on heterogeneous platforms

Devaraj, Rajesh

doi:10.1007/s11227-020-03145-w

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…In 2019, Zhang et al [206] implemented a linear programming algorithm and a dynamic programming algorithm for real-time task scheduling under a heterogeneous platform with task duplication. Subsequently, Devaraj [207] demonstrated that the linear programming algorithm fails to produce valid execution schedules because the proposed problem formulation does not correctly capture the execution requirements of real-time tasks. Devaraj [207] presented the necessary modifications for optimal execution of the algorithm proposed by Zhang et al [206].…”

Section: Schedulability Analysis Techniquesmentioning

confidence: 99%

“…Subsequently, Devaraj [207] demonstrated that the linear programming algorithm fails to produce valid execution schedules because the proposed problem formulation does not correctly capture the execution requirements of real-time tasks. Devaraj [207] presented the necessary modifications for optimal execution of the algorithm proposed by Zhang et al [206].…”

Section: Schedulability Analysis Techniquesmentioning

confidence: 99%

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A Survey of Techniques for Reducing Interference in Real-Time Applications on Multicore Platforms

et al. 2022

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This survey reviews the scientific literature on techniques for reducing interference in real-time multicore systems, focusing on the approaches proposed between 2015 and 2020. It also presents proposals that use interference reduction techniques without considering the predictability issue. The survey highlights interference sources and categorizes proposals from the perspective of the shared resource. It covers techniques for reducing contentions in main memory, cache memory, a memory bus, and the integration of interference effects into schedulability analysis. Every section contains an overview of each proposal and an assessment of its advantages and disadvantages.INDEX TERMS Real-time systems, architecture, multicore, timing analysis, schedulability analysis, WCET, co-runner interference.

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Section: Schedulability Analysis Techniquesmentioning

confidence: 99%

Section: Schedulability Analysis Techniquesmentioning

confidence: 99%

A Survey of Techniques for Reducing Interference in Real-Time Applications on Multicore Platforms

et al. 2022

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“…Dogan and Özgüner [40] analyzed the task execution reliability on heterogeneous computing systems, and combined this into applications dynamic level to implemented the scheduling algorithm. Roy et al studied the real-time precedence-constrained task scheduling problem on heterogeneous systems with multiple service levels (such as performance, reliability, cost) [34]- [36]. Zhang et al first built the heterogeneous systems communication contention and task execution reliability model.…”

Section: Related Workmentioning

confidence: 99%

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Tang

2020

IEEE Access

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Nowadays, heterogeneous computing systems have proven to be a good solution for processing computation intensive high-performance applications. The main challenges for such large-scale systems are energy consumption, computing node CPU-GPU utilization dynamic variability, and so on. In response to these challenges, this study first provides heterogeneous computing systems architecture and parallel application job model. Then, we build system computing node CPU-GPU utilization model and analyze job execution energy consumption. We also deduce the optimal CPU-GPU utilization and job deadline scheduling constraint. Third, we propose a systems CPU-GPU utilization aware and energy-efficient heuristic greedy strategy (UEJS) to solve this job scheduling problem. To improve the algorithm global optimization ability, we design a hybrid particle swarm optimization algorithm (H-PSO), which incorporates the heuristic greedy strategy into the bio-inspired search optimization technique. The rigorous experimental results clearly demonstrate that our proposed H-PSO outperforms heuristic greedy strategy, Max-EAMin, and Genetic Algorithm in terms of the average energy consumption of jobs and system job rejection rate. In particular, H-PSO is better than UEJS by 36.5%, Max-EAMin by 36.3%, and GA by 46.7% in term of the job average energy consumption for heterogeneous system with high workload.

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“…To address these problems, this study adopts a migration method that can avoid cache misses and processor task state transitions. The study focuses on the overhead nonmigration scheduling method [8] .…”

Section: Introductionmentioning

confidence: 99%

Worst-Case Response Time Analysis of Multitype DAG Tasks Based on Reconstruction

et al. 2022

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With the wide application of heterogeneous multi-core processor real-time systems, the existing analysis methods of worst-case response time (WCRT) overestimate the blocking information among tasks, resulting in a rather pessimistic estimation. To improve the accuracy of the WCRT, we propose a reconstruction-based WCRT analysis method for multi-type directed acyclic graph (DAG) tasks scheduling algorithm(RMDS). The RMDS algorithm comprises the following steps: First, we unitize all task nodes in the multi-type DAG task; Then, we use key factors as task priorities to schedule tasks and reconstruct the DAG task model into a parallel node segment model; Finally, we estimate the WCRT of multi-type DAG tasks according to the parallel node segment model to assess task schedulability. To verify the performance of our algorithm, we compared it with traditional algorithms. RDMS showed an acceptance rate 6.13% higher and its overall performance increased by 25.95% in comparison with traditional algorithms.

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A solution to drawbacks in capturing execution requirements on heterogeneous platforms

Cited by 5 publications

References 23 publications

A Survey of Techniques for Reducing Interference in Real-Time Applications on Multicore Platforms

A Survey of Techniques for Reducing Interference in Real-Time Applications on Multicore Platforms

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Worst-Case Response Time Analysis of Multitype DAG Tasks Based on Reconstruction

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