Enhancing Resource Management through Prediction-based Policies

TL;DR

This paper introduces a dynamic prediction-based policy for task-based runtime systems that optimizes core allocation, enhancing both performance and energy efficiency in multi-core systems.

Contribution

It extends task-based runtime systems with a lightweight monitoring and prediction infrastructure for dynamic core allocation, balancing performance and energy use.

Findings

Prediction-based policies outperform static policies in energy efficiency.

Dynamic core prediction maintains competitive performance.

Benchmarks show improved energy savings with minimal performance loss.

Abstract

Task-based programming models are emerging as a promising alternative to make the most of multi-/many-core systems. These programming models rely on runtime systems, and their goal is to improve application performance by properly scheduling application tasks to cores. Additionally, these runtime systems offer policies to cope with application phases that lack in parallelism to fill all cores. However, these policies are usually static and favor either performance or energy efficiency. In this paper, we have extended a task-based runtime system with a lightweight monitoring and prediction infrastructure that dynamically predicts the optimal number of cores required for each application phase, thus improving both performance and energy efficiency. Through the execution of several benchmarks in multi-/many-core systems, we show that our prediction-based policies have competitive performance while improving energy efficiency when compared to state of the art policies.