A hierarchical run-time adaptive resource allocation framework for large-scale MPSoC systems

Quan, Wei; Pimentel, Andy D.

doi:10.1007/s10617-016-9179-z

Cited by 20 publications

(20 citation statements)

References 44 publications

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“…An adaptive approach for CPU-GPU SoC has recently been presented [28], but it considers only a single application at a time and only CPU or GPU for application execution. Other adaptive approaches have also been studied [29]- [32], but they consider only one type of core and mostly do not perform evaluation on a real mobile SoC [29], [30].…”

Section: Related Workmentioning

confidence: 99%

Collaborative Adaptation for Energy-Efficient Heterogeneous Mobile SoCs

Singh

Basireddy

Prakash

et al. 2020

IEEE Trans. Comput.

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Heterogeneous Mobile System-on-Chips (SoCs) containing CPU and GPU cores are becoming prevalent in embedded computing, and they need to execute applications concurrently. However, existing run-time management approaches do not perform adaptive mapping and thread-partitioning of applications while exploiting both CPU and GPU cores at the same time. In this paper, we propose an adaptive mapping and thread-partitioning approach for energy-efficient execution of concurrent OpenCL applications on both CPU and GPU cores while satisfying performance requirements. To start execution of concurrent applications, the approach makes mapping (number of cores and operating frequencies) and partitioning (distribution of threads between CPU and GPU) decisions to satisfy performance requirements for each application. The mapping and partitioning decisions are made by having a collaboration between the CPU and GPU cores' processing capabilities such that balanced execution can be performed. During execution, adaptation is triggered when new application(s) arrive, or an executing one finishes, that frees cores. The adaptation process identifies a new mapping and thread-partitioning in a similar collaborative manner for remaining applications provided it leads to an improvement in energy efficiency. The proposed approach is experimentally validated on the Odroid-XU3 hardware platform with varying set of applications. Results show an average energy saving of 37%, compared to existing approaches while satisfying the performance requirements.

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Section: Related Workmentioning

confidence: 99%

Collaborative Adaptation for Energy-Efficient Heterogeneous Mobile SoCs

Singh

Basireddy

Prakash

et al. 2020

IEEE Trans. Comput.

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“…The resource dynamic management process is carried out by employing a centralized [Nollet et al 2008;Carvalho et al 2010;Ng et al 2015], distributed [Peter et al 2009;Kobbe et al 2011;Castilhos et al 2013], or hierarchical [Götzinger et al 2016;Quan and Pimentel 2016] approach. In centralized management, one core of the platform is used as the manager that handles the allocation process.…”

Section: Classification Of Dynamic Resource Allocation Strategiesmentioning

confidence: 99%

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

et al. 2017

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Multi/Many-core systems are envisioned to satisfy the ever increasing performance requirements of complex applications in various domains such as embedded and high performance computing (HPC). Such systems need to cater for increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies in order to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed over the last two decades and highlights the emerging trends for multi/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles such as market and biological concepts to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided.

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“…In [21], run-time adaptation consists in mapping long-lasting jobs onto a multi-processor systemon-chip architecture by making use of configurations elaborated at design-time. However, in our case the run-time management system operates in a non-periodic context by taking very frequent mapping decisions for computing jobs with a very limited duration.…”

Section: Embedded Systemsmentioning

confidence: 99%

Communication-Aware Prediction-Based Online Scheduling in High-Performance Real-Time Embedded Systems

Goupille-Lescar

Lenormand

Parlavantzas

et al. 2018

Algorithms and Architectures for Parallel Processing

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Current high-end, data-intensive real-time embedded sensor applications (e.g., radar, optronics) require very specific computing platforms. The nature of such applications and the environment in which they are deployed impose numerous constraints, including realtime constraints, and computing throughput and latency needs. Static application placement is traditionally used to deal with these constraints. However, this approach fails to provide adaptation capabilities in an environment in constant evolution. Through the study of an industrial radar use-case, our work aims at mitigating the aforementioned limitations by proposing a low-latency online resource manager derived from techniques used in large-scale systems, such as cloud and grid environments. The resource manager introduced in this paper is able to dynamically allocate resources to fulfill requests coming from several sensors, making the most of the computing platform while providing guaranties on non-functional properties and Quality of Service (QoS) levels. Thanks to the load prediction implemented in the manager, we are able to achieve a 83% load increase before overloading the platform while managing to reduce ten times the incurred QoS penalty. Further methods to reduce the impact of the overload are as well as possible future improvements are proposed and discussed.

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A hierarchical run-time adaptive resource allocation framework for large-scale MPSoC systems

Cited by 20 publications

References 44 publications

Collaborative Adaptation for Energy-Efficient Heterogeneous Mobile SoCs

Collaborative Adaptation for Energy-Efficient Heterogeneous Mobile SoCs

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

Communication-Aware Prediction-Based Online Scheduling in High-Performance Real-Time Embedded Systems

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