17532

Multi-Tasking Scheduling for Heterogeneous Systems

Yuan Wen
Institute of Computing Systems Architecture, School of Informatics, University of Edinburgh
University of Edinburgh, 2017

@article{wen2017multi,

   title={Multi-tasking scheduling for heterogeneous systems},

   author={Wen, Yuan},

   year={2017},

   publisher={The University of Edinburgh}

}

Download Download (PDF)   View View   Source Source   

2040

views

Heterogeneous platforms play an increasingly important role in modern computer systems. They combine high performance with low power consumption. From mobiles to supercomputers, we see an increasing number of computer systems that are heterogeneous. The most well-known heterogeneous system, CPU+GPU platforms have been widely used in recent years. As they become more mainstream, serving multiple tasks from multiple users is an emerging challenge. A good scheduler can greatly improve performance. However, indiscriminately allocating tasks based on availability leads to poor performance. As modern GPUs have a large number of hardware resources, most tasks cannot efficiently utilize all of them. Concurrent task execution on GPU is a promising solution, however, indiscriminately running tasks in parallel causes a slowdown. This thesis focuses on scheduling OpenCL kernels. A runtime framework is developed to determine where to schedule OpenCL kernels. It predicts the best-fit device by using a machine learning-based classifier, then schedules the kernels accordingly to either CPU or GPU. To improve GPU utilization, a kernel merging approach is proposed. Kernels are merged if their predicted co-execution can provide better performance than sequential execution. A machine learning based classifier is developed to find the best kernel pairs for co-execution on GPU. Finally, a runtime framework is developed to schedule kernels separately on either CPU or GPU, and run kernels in pairs if their co-execution can improve performance. The approaches developed in this thesis significantly improve system performance and outperform all existing techniques.
No votes yet.
Please wait...

* * *

* * *

Featured events

2018
November
27-30
Hida Takayama, Japan

The Third International Workshop on GPU Computing and AI (GCA), 2018

2018
September
19-21
Nagoya University, Japan

The 5th International Conference on Power and Energy Systems Engineering (CPESE), 2018

2018
September
22-24
MediaCityUK, Salford Quays, Greater Manchester, England

The 10th International Conference on Information Management and Engineering (ICIME), 2018

2018
August
21-23
No. 1037, Luoyu Road, Hongshan District, Wuhan, China

The 4th International Conference on Control Science and Systems Engineering (ICCSSE), 2018

2018
October
29-31
Nanyang Executive Centre in Nanyang Technological University, Singapore

The 2018 International Conference on Cloud Computing and Internet of Things (CCIOT’18), 2018

HGPU group © 2010-2018 hgpu.org

All rights belong to the respective authors

Contact us: