@article{Lin2019, 
author = {Han Lin and Ming-Fan Li and Cheng-Fan Jia and Jun-Nan Liu and Hong An},
title = {Degree-of-Node Task Scheduling of Fine-Grained Parallel Programs on Heterogeneous Systems},
year = {2019},
journal = {Journal of Computer Science and Technology},
volume = {34},
number = {5},
pages = {1096-1108},
keywords = {task scheduling, heterogeneous system, performance, parallel program},
url = {https://www.sciopen.com/article/10.1007/s11390-019-1962-4},
doi = {10.1007/s11390-019-1962-4},
abstract = {Processor specialization has become the development trend of modern processor industry. It is quite possible that this will still be the main-stream in the next decades of semiconductor era. As the diversity of heterogeneous systems grows, organizing computation efficiently on systems with multiple kinds of heterogeneous processors is a challenging problem and will be a normality. In this paper, we analyze some state-of-the-art task scheduling algorithms of heterogeneous computing systems and propose a Degree of Node First (DONF) algorithm for task scheduling of fine-grained parallel programs on heterogeneous systems. The major innovations of DONF include: 1) simplifying task priority calculation for directed acyclic graph (DAG) based fine-grained parallel programs which not only reduces the complexity of task selection but also enables the algorithm to solve the scheduling problem for dynamic DAGs; 2) building a novel communication model in the processor selection phase that makes the task scheduling much more efficient. They are achieved by exploring finegrained parallelism via a dataflow program execution model, and validated through experimental results with a selected set of benchmarks. The results on synthesized and real-world application DAGs show a very good performance. The proposed DONF algorithm significantly outperforms all the evaluated state-of-the-art heuristic algorithms in terms of scheduling length ratio (SLR) and efficiency.}
}