Two-level utilization-based processor allocation for scheduling moldable jobs

Wu, Ying-Jhih; Yu, Shuo-Ting; Lai, Kuan‐Chou; Chhabra, Amit; Chang, Hsi-Ya; Huang, Kuo-Chan

doi:10.1007/s11227-020-03246-6

Cited by 2 publications

(1 citation statement)

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“…The number of tasks of a job is fixed and equal to the Degree of Parallelism (DoP). In a rigid job, the number of processing tasks is predefined and fixed [31]. In this type of job, changing the processing core at run-time is inapplicable.…”

Section: Workload Modelmentioning

confidence: 99%

HDSAP: Heterogeneity-aware dynamic scheduling algorithm to improve performance of nano-scale many-Core processors for unpredictable workloads

Kia

Rajabzadeh

2022

Preprint

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The performance growth in processors has been continuing toward increasing the number of processing cores on the chip and scaling the feature size of transistors. However, in the nano era, side effects of the scaling, such as induced heterogeneities in the performance, power, and soft error rate of identically designed cores, prevent the potential performance from being fully utilized. In this paper, we harness the mentioned side effects in shared-memory multicore processors with unpredictable workloads by a dynamic heuristic scheduling algorithm called HDSAP. The aim of HDSAP is to maximize performance, i.e. the average response time, under power and reliability constraints in presence of induced heterogeneities. In this regard, we use a mathematical model to quantify task to core assignments based on performance variation. We also consider the variation in power to change selected cores when the power constraint is missed. To meet the reliability constraint, we use N-Modular redundancy while being aware of the variation in soft error rate of cores to prevent under/over reliability estimation. To evaluate HDSAP, we run SPLASH benchmark suite on Sniper and MACPat simulators. As a result, the response time of HDSAP reduces by 6%, 8%, and 25% in comparison with similar algorithms under the same power and reliability constraints.

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Section: Workload Modelmentioning

confidence: 99%