Fast network communities visualization on massively parallel GPU architecture

M. Mrzek, B. Jerman Blazic
Jozef Stefan Institute, Ljubljana, Slovenia
36th International Convention on Information and Communication Technology, Electronics and Microelectronics, 2013

   title={Fast network communities visualization on massively parallel GPU architecture},

   author={Mr{v{z}}ek, M and Bla{v{z}}i{v{c}}, B Jerman},



Download Download (PDF)   View View   Source Source   



Modeling phenomena with networks has a wide application in many disciplines including biology, economics, sociology, and computer science. In network analysis modularity is an important measure for automatically extracting communities of closely connected nodes. Another important aspect of the network analysis is network visualization. Different techniques for network layout generation exist and the force-driven layout is one of the most popular ones. However, generating force-driven layouts of large networks is both time consuming and can produce a layout where distinct communities of nodes are not separated, but rather remain untangled. Such layouts are harder to be visually inspected by an end-user. In this paper, we propose a GPU-based implementation of a force-driven algorithm for layout generation. By exploiting the massively parallel architecture of modern GPUs we reduce the computational time by orders of magnitude compared with the CPU-based implementation. Secondly, we implement a multi-layer force-driven method for network layout generation where communities are less entangled. Again, by exploiting the GPU we obtain significant speed-up of computation over the CPU implementations. Our results imply that GPUs can speed up significantly the computations in network analysis and thus larger networks can be analyzed in real-time.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1660 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 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: