A Graph-Partition-Based Scheduling Policy for Heterogeneous Architectures

TL;DR

This paper introduces a graph-partition-based scheduling policy designed to efficiently map data-flow workloads onto heterogeneous multi-core and many-core architectures, addressing the challenge of task scheduling in distributed systems.

Contribution

It proposes a novel graph-partition scheduling approach that improves task mapping efficiency on heterogeneous architectures, with performance comparable to traditional methods.

Findings

Achieves comparable performance to queue-based scheduling.

Effectively maps data-flow workloads onto heterogeneous hardware.

Provides a new approach for task scheduling in distributed systems.

Abstract

In order to improve system performance efficiently, a number of systems choose to equip multi-core and many-core processors (such as GPUs). Due to their discrete memory these heterogeneous architectures comprise a distributed system within a computer. A data-flow programming model is attractive in this setting for its ease of expressing concurrency. Programmers only need to define task dependencies without considering how to schedule them on the hardware. However, mapping the resulting task graph onto hardware efficiently remains a challenge. In this paper, we propose a graph-partition scheduling policy for mapping data-flow workloads to heterogeneous hardware. According to our experiments, our graph-partition-based scheduling achieves comparable performance to conventional queue-base approaches.