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

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Abstract

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 nanoera, 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 unknown workloads by a dynamic heuristic scheduling algorithm called HDSAP. HDSAP aims 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 the 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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Notes

  1. In this paper, we refer to a job as the incoming workload. A job is a set of dependent tasks and the type of dependency is the barrier–synchronization.

  2. Unknown workload refers to the workload in which the arrival time, departure time, and execution time of jobs are not known to the scheduler in advance.

  3. Since in this paper, we do not consider the effect of the memory controller on the execution time, we eliminate this effect by setting the memory controller latency to zero. We will consider it in our future studies.

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran

    Keihaneh Kia & Amir Rajabzadeh

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  1. Keihaneh Kia
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  2. Amir Rajabzadeh
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Correspondence to Amir Rajabzadeh.

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Kia, K., Rajabzadeh, A. HDSAP: heterogeneity-aware dynamic scheduling algorithm to improve performance of nanoscale many-core processors for unknown workloads. J Supercomput 79, 13341–13369 (2023). https://doi.org/10.1007/s11227-023-05159-6

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