Building Time-Triggered Schedules for typed-DAG Tasks with alternative implementations

TL;DR

This paper introduces a time-triggered scheduling method using ILP for HPC-DAG tasks on heterogeneous multi-core platforms, addressing heterogeneity, execution costs, and aiming for efficient real-time application deployment.

Contribution

It presents a novel ILP-based scheduling approach for HPC-DAG tasks on heterogeneous cores, accommodating alternative implementations and online execution considerations.

Findings

Effective scheduling of HPC-DAG tasks on heterogeneous platforms.

Reduced solving time through gradual design space exploration.

Enhanced handling of heterogeneity and execution costs.

Abstract

Hard real-time systems like image processing, autonomous driving, etc. require an increasing need of computational power that classical multi-core platforms can not provide, to fulfill with their timing constraints. Heterogeneous Instruction Set Architecture (ISA) platforms allow accelerating real-time workloads on application-specific cores (e.g. GPU, DSP, ASICs) etc. and are suitable for these applications. In addition, these platforms provide larger design choices as a given functionnality can be implemented onto several types of compute elements. HPC-DAG (Heterogeneous Parallel Directed Acyclic Graph) task model has been recently proposed to capture real-time workload execution on heterogeneous platforms. It expresses the ISA heterogeneity, and some specific characteristics of hardware accelerators, as the absence of preemption or costly preemption, alternative implementations and on-line conditional execution. In this paper, we propose a time-table scheduling approach to allocate and schedule a set of HPC-DAG tasks onto a set of heterogeneous cores, by the mean Integer Linear Programming (ILP). Our design allows to handle heterogeniety of resources, on-line execution costs, and a faster solving time, by exploring gradually the design space