DASH: Dynamic Scheduling Algorithm for Single-ISA Heterogeneous Nano-scale Many-Cores

Kia, Keihaneh; Rajabzadeh, Amir

doi:10.1109/iccke50421.2020.9303673

Cited by 2 publications

(2 citation statements)

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“…The authors handle this trade-off with a Pareto-optimal scheduler with the complexity of O(k.n 3 ). In DASH [25], we have considered heterogeneity in frequency and power caused by process variation and also the communication cost in SNUCA architecture under power limitation. Contrary to HDSAP, it exploits DVFS to improve performance (i.e.…”

Section: Related Workmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

“…Aj← remapping(Aj) //call remapping end if 25. if(Aj=φ) //remapping can't find a new set of cores for jobj execution within the PB do 26.…”

mentioning

confidence: 99%

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

Kia

Rajabzadeh

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

HDSAP: heterogeneity-aware dynamic scheduling algorithm to improve performance of nanoscale many-core processors for unknown workloads

Kia

Rajabzadeh

2023

J Supercomput

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DASH: Dynamic Scheduling Algorithm for Single-ISA Heterogeneous Nano-scale Many-Cores

Cited by 2 publications

References 18 publications

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

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

HDSAP: heterogeneity-aware dynamic scheduling algorithm to improve performance of nanoscale many-core processors for unknown workloads

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