Scalable Optimal Greedy Scheduler for Asymmetric Multi-/Many-Core Processors

Venkataramani, Vanchinathan; Pathania, Anuj; Mitra, Tulika

doi:10.1007/978-3-030-27562-4_9

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…The problem of optimal execution of multiple applications is well-known. The common aim is to increase the overall throughput by using historical data [9], [10], or by exploiting the concavity of throughput of multi-threaded applications [11]. These works do not optimize energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…The loop in lines 5-18 schedules the job on already constructed mapping segments in the ascending order of the time segments (with a slight abuse of notation). After checking resource constraints on the segment (line 7), the algorithm checks whether the job will execute during the whole segment (lines [10][11] or only a part of it (lines 13-17). In the last case, the mapping segment is split at time the job At the end, line 23 verifies that the job meets its deadline.…”

Section: System Model and Problem Definitionmentioning

confidence: 99%

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Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping

Khasanov

Castrillón

2020

2020 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Modern embedded computing platforms consist of a high amount of heterogeneous resources, which allows executing multiple applications on a single device. The number of running application on the system varies with time and so does the amount of available resources. This has considerably increased the complexity of analysis and optimization algorithms for runtime mapping of firm real-time applications. To reduce the runtime overhead, researchers have proposed to pre-compute partial mappings at compile time and have the runtime efficiently compute the final mapping. However, most existing solutions only compute a fixed mapping for a given set of running applications, and the mapping is defined for the entire duration of the workload execution. In this work we allow applications to adapt to the amount of available resources by using mapping segments. This way, applications may switch between different configurations with varied degree of parallelism. We present a runtime manager for firm real-time applications that generates such mapping segments based on partial solutions and aims at minimizing the overall energy consumption without deadline violations. The proposed algorithm outperforms the state-of-theart approaches on the overall energy consumption by up to 13% while incurring an order of magnitude less scheduling overhead.

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Section: Related Workmentioning

confidence: 99%

Section: System Model and Problem Definitionmentioning

confidence: 99%

Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping

Khasanov

Castrillón

2020

2020 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Runtime Resource Management with Multiple-Step-Ahead Workload Prediction

Niknafs

Eles

Peng

2023

ACM Trans. Embed. Comput. Syst.

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Modern embedded platforms need sophisticated resource managers in order to utilize their heterogeneous computational resources efficiently. Furthermore, such platforms are subject to fluctuating workloads that are unforeseeable at design time. Predicting the incoming workload could enhance the efficiency of resource management in this situation. But is that the case? And, if so, how substantial is this improvement? Does multiple-step-ahead prediction of the workload contribute to this improvement? How precise must the prediction be in order to improve decisions rather than cause harm? By proposing a prediction-based resource manager that aims at meeting task deadlines while minimizing energy usage, and by conducting extensive tests, we attempt to provide answers to the aforementioned questions.

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Scalable Optimal Greedy Scheduler for Asymmetric Multi-/Many-Core Processors

Cited by 2 publications

References 21 publications

Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping

Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping

Runtime Resource Management with Multiple-Step-Ahead Workload Prediction

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