10436

A Feedback Approach to Task Partitioning in Heterogeneous Architectures

Yasir Ali, Zuhair Qadir
Department of Computer Sciences, School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
Lahore University of Management Sciences, 2013
@article{ali2013feedback,

   title={A Feedback Approach to Task Partitioning in Heterogeneous Architectures},

   author={Ali, Yasir and Qadir, Zuhair},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

416

views

Personal Computers of today are based on complex architectures often with multiple high performance computational units for various dedicated purposes. The General Purpose GPU is one such example where Graphic Processing Units are being used for more general purpose computing. In this paper, we target such architectures and focus on Load Balancing and Task Partitioning on Heterogeneous Architectures. We present some related implementations, discussing a specific implementation in detail and present our proposal as an improvement over the system model. We present the idea of a State of Equilibrium for the machine, where a feedback based task partitioning system keeps the system load over multiple computation devices balanced and optimized for maximum performance.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

166 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1272 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: