Assembly-Free Large-Scale Modal Analysis on the GPU

Praveen Yadav, Krishnan Suresh
Department of Mechanical Engineering, UW-Madison, Madison, Wisconsin 53706, USA
JCISE, 2012

   title={Assembly-Free Large-Scale Modal Analysis on the GPU},

   author={Yadav, P. and Suresh, K.},



Download Download (PDF)   View View   Source Source   



Popular eigen-solvers such as block-Lanczos require repeated inversion of an eigen-matrix. This is a bottleneck in large-scale modal problems with millions of degrees of freedom. On the other hand, the classic Rayleigh-Ritz conjugate gradient method only requires a matrix-vector multiplication, and is therefore potentially scalable to such problems. However, as is well-known, the Rayleigh-Ritz has serious numerical deficiencies, and has largely been abandoned by the finite element community. In this paper, we address these deficiencies through subspace augmentation, and consider a subspace augmented Rayleigh-Ritz conjugate gradient method (SaRCG). SaRCG is numerically stable and does not entail explicit inversion. As a specific application, we consider the modal analysis of geometrically complex structures discretized via non-conforming voxels. The resulting large-scale eigen-problems are then solved via SaRCG. The voxelization structure is also exploited to render the underlying matrix-vector multiplication assembly-free. The implementation of SaRCG on multi-core CPUs, and graphics-programmable-units (GPUs) is discussed, followed by numerical experiments and case-studies.
VN:F [1.9.22_1171]
Rating: 5.0/5 (1 vote cast)
Assembly-Free Large-Scale Modal Analysis on the GPU, 5.0 out of 5 based on 1 rating

* * *

* * *

Follow us on Twitter

HGPU group

1544 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

276 people like HGPU on Facebook

* * *

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: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • 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: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

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 © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: