9300

Multigrid Optimization Methods for High Performance Computing

Christian Wagner
University Trier
University Trier, 2012
@article{wagner2012multigrid,

   title={Multigrid Optimization Methods for High Performance Computing},

   author={Wagner, Christian},

   year={2012}

}

Download Download (PDF)   View View   Source Source   

364

views

The aim of this work was the investigation of implementability and efficiency of an algorithm for solving optimal control problems on a new hardware architecture. For an academic test problem the collective smoothing multigrid method (CSMG) was realized on a commodity graphics card (GPU) and the performance in term of elapsed time compared to those on a recent CPU. For dealing with large problem size, new algorithms were designed and two different approaches considered: on the one hand, a recursive approach and on the other hand, a simultaneous approach. Both are based on a nonoverlapping domain decomposition of the entire domain into two subdomains, where a discrete approximation of the Steklov-Poincare operator is derived by a Schur complement method. This so-called capacitance matrix is computed efficiently and inverted analytically. Numerical results show the performance of the CSMG for the one domain case and for both of the developed domain decomposition methods, comparing GPU and CPU. For large-scale optimization, an optimal control problem with ~248 Mio. unknowns was solved by dividing the entire domain into 8 subdomains and processed on 8 GPUs/CPUs in parallel as a proof on concept.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

128 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1193 peoples are following HGPU @twitter

Featured events

* * *

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: