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Performance and Power Optimization for Multi-core Systems using Multi-level Scaling

  • Author(s): Almatouq, Munirah
  • Advisor(s): Gaudiot, Jean-Luc
  • et al.
Creative Commons Attribution 4.0 International Public License
Abstract

Integrating more cores per chip to increase the performance of processors has been trending

for the past decade. However, this trend cannot be sustained because the reduction in power

consumption per core has slowed down while the power budget per chip has not increased.

Modern processor chips are becoming so power constrained to the point that not all their

devices can be powered at once - this is often referred to as dark silicon. To maximize

performance within these power constraints, the system must carefully select the set of

resources to be used.

To solve this problem, several power management techniques such as Dynamic Voltage/Frequency

Scaling (DVFS), core scaling, and resource scaling have been the subject of active research

and have proven to be effective. However, most of these solutions are sub-optimal because

they explore only one layer of the architecture. Although considering one layer reduces the

complexity of the technique, it limits the exploitation of potential improvement in performance

and energy consumption.

The problem is an order of magnitude more complex for power constrained multi-core architectures.

We need power management systems that can take advantage of dierent scaling

techniques. Many studies have been conducted on scaling with the sole objective of performance

improvement. Nevertheless, few of them have considered both performance and energy consumption in the optimization process.

This dissertation proposes an optimization technique that balances performance and energy

consumption by applying a joint control of core, resource and frequency scaling. This

system finds the optimal configuration for a given application and accordingly adapts the

architecture configuration.

The proposed technique consists of three stages: configuration sampling, response surface

models to approximate performance and energy consumption, and online optimization using

a genetic algorithm (GA). To evaluate the system, experiments were conducted on a

simulated 12 core architecture. Our experiments have shown that the performance could

improve by 15% on average while achieving energy savings of up to 26%. Using a per-core

configuration improves the performance by 25% on average and reduces the energy by 18%.

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