Compute Distance Matrices with GPU

Seongho Kim, Ming Ouyang
Bioinformatics and Biostatistics Department, University of Louisville, Louisville, Kentucky 40292, USA
Third Annual International Conference on Advances in Distributed and Parallel Computing (ADPC 2012), 2012

   title={Compute Distance Matrices with GPU},

   author={Kim, Seongho and Ouyang, Ming},



Download Download (PDF)   View View   Source Source   



Given a data matrix where the rows are objects and the columns are variables, researchers often want to compute all the pairwise distances among the objects. Due to the design of Nvidia GPU architecture, CUDA code can work with ease data matrices where the numbers of rows and columns are multiples of sixteen. The present work proposes a padding strategy that add additional rows and columns of zeros to the matrix so that a matrix of any size may be processed by a simple and fast CUDA kernel function. For Pearson correlation coefficient, the GPU computation 15.9 to 33.5 times faster than the CPU.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1662 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

337 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: