Workload Partitioning Algorithm Based on Performance Curve of GPU in Heterogeneous Platforms
- 10.2991/acaai-18.2018.45How to use a DOI?
- GPU; hybrid system; data parallel applications; workload partitioning
With the development of GPU's general computing power, hybrid systems composed of multi-core CPU and GPU are becoming more and more popular in data parallel applications. Because the performance of GPU is related to the magnitude of the load received, effective load allocation methods are very important for improving the performance of data parallel applications. The existing static load distribution methods fail to use the characteristics effectively - GPU performance changed with the load, causing the load unbalanced. Dynamic load distribution methods easily reduce the performance of the system due to the excessive synchronization and data transmission operation. In this paper, we propose a new workload partitioning algorithm, which takes advantage of the characteristics of GPU performance varying with the workload in off-line analysis stage, and uses the successive decreasing method to determine the optimal load allocation ratio between multi-core CPU and GPU. The effectiveness of the load allocation algorithm is verified on the remote sensing data set based on the median filtering algorithm.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hongyu Yang AU - Hui Chen AU - Chengming Li AU - Qingshan Jiang AU - Xueyuan Cai PY - 2018/03 DA - 2018/03 TI - Workload Partitioning Algorithm Based on Performance Curve of GPU in Heterogeneous Platforms BT - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018) PB - Atlantis Press SP - 191 EP - 195 SN - 1951-6851 UR - https://doi.org/10.2991/acaai-18.2018.45 DO - 10.2991/acaai-18.2018.45 ID - Yang2018/03 ER -