Accelerating Graph Analysis with Heterogeneous Systems

Ate Penders
Department of Software and Computer Technology, Delft University of Technology
Delft University of Technology, 2012

   title={Accelerating Graph Analysis with Heterogeneous Systems},

   author={Penders, Ate},



Download Download (PDF)   View View   Source Source   



Data analysis is a rising field of interest for computer science research due to the growing amount of information that is digitally available. This increase in data has as direct consequence that any analysis is significantly complex. By using structured representations for the data sets, like graphs, the analysis becomes feasible, but is still time-consuming. In this project, the focus is on the reduction of the computational time for data analysis, with the introduction of accelerators. Accelerators are specialized hardware components that assist the general processing unit in performing (parts of) the task at hand. In particular, we focus on the use of General Purpose Graphical Processing Units (GPGPU) to help speedup the analysis. GPUs are specifically designed for representing and manipulating graphical data which invoke the processing of large chunks of data, GPUs are designed with large numbers of concurrent processing units and thus have a high potential of improving performance. In this project, we show the impact of using GPGPUs for both simple and more complex analysis, varying from small to large data sets, with the use of a programming model called OpenCL. We compare the performance of using accelerators against the traditional CPU-based implementation. Due to the inter-platform portability of the OpenCL model, such comparison can be performed without having to alter the algorithm. The use of accelerators is expected to become beneficial for analysis that require large computational power. For example, search algorithms (that require little to no computation) are not expected to profit from accelerators, while the more complex, centrality analysis is expected to have significantly more benefit from accelerators. Our results clearly shows this shift of performance improvement when algorithms further utilize the potential of accelerators, because the analysis grows in size and/ or complexity.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

218 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1406 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: 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 12.2
  • SDK: 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 © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: