Combining application adaptivity and system-wide Resource Management on multi-core platforms

Massari, Giuseppe; Paone, Edoardo; Bellasi, Patrick; Palermo, Gianluca; Zaccaria, Vittorio; Fornaciari, William; Silvano, Cristina

doi:10.1109/samos.2014.6893191

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Ascia et al [16] were one of the first to propose multiobjective mapping generation, using evolutionary algorithms to find Pareto-optimal points. Approaches such as [3], [17]- [19] use heuristics for efficient exploration of Pareto-optimal configurations, while others such as [4], [20] use evolutionary algorithms. The amount of considered configuration can be also reduced by exploiting system symmetries [21], [22].…”

Section: Related Workmentioning

confidence: 99%

“…Ykman-Couvreur et al [5], for instance, propose a fast heuristic, which expresses the resource demands of operating points as a single value, and then use a greedy algorithm to solve the MMKP. This heuristic underlies the solutions proposed in [17], [20]. Wildermann et al [6], [23] solve the problem by applying a Lagrangian relaxation method.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…Given the size of the solution space in fact, the time needed to find good solutions, can be often too long for considering the execution of the policy at run-time. The BarbequeRTRM allows us to perform this exploration and insert the set of mapping solutions found into a specific file, called recipe [20]. This file is used to specify both the per-task requirements and, optionally, a set of resource mapping solutions that the resource manager should consider at run-time.…”

Section: Hierarchical Policy Definition and Updatementioning

confidence: 99%

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

Massari

Pupykina

Agosta

et al. 2019

Lecture Notes in Computer Science

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High-Performance Computing (HPC) is rapidly moving towards the adoption of nodes characterized by an heterogeneous set of processing resources. This has already shown benefits in terms of both performance and energy efficiency. On the other side, heterogeneous systems are challenging from the application development and the resource management perspective. In this work, we discuss some outcomes of the MANGO project, showing the results of the execution of real applications on a emulated deeply heterogeneous systems for HPC. Moreover, we assessed the achievements of a proposed resource allocation policy, aiming at identifying a priori the best resource allocation options for a starting application.

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Runtime resource management for embedded and HPC systems

Fornaciari

Pozzi

Reghenzani

et al. 2016

Proceedings of the 7th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and the

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Resource management is a well known problem in almost every computing system ranging from embedded to High Performance Computing (HPC) and is useful to optimize multiple orthogonal system metrics such as power consumption, performance and reliability. To achieve such an optimization a resource manager must suitably allocate the available system resources - e.g. processing elements, memories and interconnect - to the running applications. This kind of process incurs in two main problems: a) system resources are usually shared between multiple applications and this induces resource contention; and b) each application requires a different Quality of Service, making it harder for the re- source manager to work in an application-agnostic mode. In this scenario, resource management represents a critical and essential component in a computing system and should act at different levels to optimize the whole system while keeping it exible and versatile. In this paper we describe a multi-layer resource management strategy that operates at application, operating system and hardware level and tries to optimize resource allocation on embedded, desktop multi-core and HPC systems

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Combining application adaptivity and system-wide Resource Management on multi-core platforms

Cited by 4 publications

References 14 publications

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

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

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

Runtime resource management for embedded and HPC systems

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