부산대학교 도서관

Real-time and latency sensitive applications such as autonomous driving, feature an increasing need of computational power that traditional multi-core platforms can not provide. For this purpose, many heterogeneous embedded platforms have been released recently. They offer a set of diverse processing elements (e.g. GPUs, DSPs, ASICs, etc...) in order to manage the computational demands of data hungry applications. The system engineer, therefore, can choose the fittest processing element for each specific subtask. In this context, timing constraints and related task models are of paramount importance.The HPC-DAG (Heterogeneous Parallel Directed Acyclic Graph) task model has been recently proposed to capture real-time workload execution on modern heterogeneous platforms. It expresses the Instruction Set Architecture (ISA) heterogeneity across the different compute accelerators, but also their differences in terms of possible scheduling policies such as preemption.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 of Integer Linear Programming (ILP). Our design allows the system engineer to handle heterogeneity of resources, of on-line execution costs, and of a part of the tasks and sub-tasks allocation to cores. It improves the solving time compared to the state of the art by gradually exploring the design space.