A hybrid evolutionary approach for heterogeneous multiprocessor scheduling

Goh, C. J.; Teoh, E.J.; Tan, Kay Chen

doi:10.1007/s00500-008-0356-2

Cited by 14 publications

(8 citation statements)

References 26 publications

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“…There also exist some reported works on the hybridization of both deterministic and non-deterministic algorithms for multiprocessor scheduling [16,44]. Yu in [44] proposes an AIS-based algorithm for task scheduling in heterogeneous multiprocessor systems, in which the scheduling process is separated into two phases, task-processor mapping and task sequence assignment.…”

Section: Related Workmentioning

confidence: 98%

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Wen

Yang

2011

Information Sciences

103

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Section: Related Workmentioning

confidence: 98%

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Wen

Yang

2011

Information Sciences

103

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“…Much research has been done using heterogeneous computing systems to meet energy minimization goals. Several algorithms sought to determine an application schedule offline [17,33,34] and online [6,11,15,16]. Kumar et al [16] used a heuristic to schedule applications to different cores.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Scheduling for Heterogeneous Multicores

et al. 2021

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Heterogeneous multicore systems can help adherence to design goals by providing a diverse set of hardware components to meet application requirements. Each core may also have tunable hardware that can reconfigured for different applications. However, scheduling becomes difficult in the presence of tunable hardware due to the additional constraint that an application must be scheduled to a core that offers the best configuration, based on the application's requirements, to maximize the benefit of the tunable hardware. The scheduling decision can be made by exploring and evaluating each hardware configuration in the design space exhaustively or through the use of a heuristic to evaluate a subset of the design space. However, exhaustive and heuristic approaches do not scale well with the number of available hardware configurations. To improve upon exhaustive and heuristic approaches, in this paper, we present a dynamic scheduling methodology that uses machine learning to schedule applications to the application's respective best core for reduced energy consumption for a system with configurable caches. We use an artificial neural network (ANN) to train our predictive model, using hardware counters, to predict the best core and a tuning heuristic to determine best configuration on non-best cores. If the best core is busy, our scheduler considers alternative idle cores, or the application is stalled depending on which decision is energy advantageous. Our experiments show that our proposed system can achieve a total energy savings of approximately 33% and 2.5% compared to a fixed-cache, base system when using a configurable instruction and data cache, respectively, while remaining competitive and exhibiting better scaling properties compared to systems adopting a heuristic approach.

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“…Many of these scheduling algorithms are static (off-line) [7], [8], [9], [10], [11], while others perform dynamic (online) scheduling [12], [13], [14], [15], [16], [17]. These dynamic approaches have the benefit that they can adapt to the dynamic workload and the different phases of applications.…”

Section: B Mapping Applications To Coresmentioning

confidence: 99%

Phase-Guided Scheduling on Single-ISA Heterogeneous Multicore Processors

Sawalha

Wolff

Tull

et al. 2011

2011 14th Euromicro Conference on Digital System Design

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Single-ISA heterogeneous (also known as asymmetric) multicore processors offer significant advantages over homogenous multicores in terms of both power and performance. Power-efficient cores can be paired with higherperformance cores to achieve advantageous power/performance tradeoffs. Unfortunately, such processors also create unique challenges in effective mapping of processes to cores. The greater the diversity of cores, the more complex this problem becomes. Previous scheduling approaches sample performance while permuting the schedule across each type of core each time a change in application behavior is detected. However, approaches that require frequent sampling of the performance of threads (or combinations of threads) on each core may be impractical. We propose scheduling threads on a heterogeneous multicore processor using not just the detection of a change in program behavior or phase, but instead an identification and recording of these phase behaviors. We highlight the correlation between the execution phases of an application and the performance of those phases on any particular core type. We present mechanisms that exploit this correlation between program phases and appropriate scheduling decisions and demonstrate near optimal mapping of thread segments to processor cores can be done without frequently sampling the performance of each thread on each processor core type.

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A hybrid evolutionary approach for heterogeneous multiprocessor scheduling

Cited by 14 publications

References 26 publications

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Dynamic Scheduling for Heterogeneous Multicores

Phase-Guided Scheduling on Single-ISA Heterogeneous Multicore Processors

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