9524

Accelerating Fast Fourier Transform for Wideband Channelization

Carlo del Mundo, Vignesh Adhinarayanan, Wu-chun Feng
Department of Electrical & Computer Engineering
IEEE International Conference on Communications (ICC), 2013
@article{del2013accelerating,

   title={Accelerating Fast Fourier Transform for Wideband Channelization},

   author={del Mundo, Carlo and Adhinarayanan, Vignesh and Feng, Wu-chun},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

396

views

Wideband channelization is a compute-intensive task with performance requirements that are arguably greater than what current multi-core CPUs can provide. To date, researchers have used dedicated hardware such as field programmable gate arrays (FPGAs) to address the performancecritical aspects of the channelizer. In this work, we assess the viability of the graphics processing unit (GPU) to achieve the necessary performance. In particular, we focus on the fast Fourier Transform (FFT) stage of a wideband channelizer. While there exists previous work for FFT on a NVIDIA GPU, the substantially higher peak floating-point performance of an AMD GPU has been less explored. Thus, we consider three generations of AMD GPUs and provide insight into the optimization of FFT on these platforms. Our architecture-aware approach across three different generations of AMD GPUs outperforms a multithreaded Intel Sandy Bridge CPU with vector extensions by factors of 4.3, 4.9, and 6.6 on the Radeon HD 5870, 6970, and 7970, respectively.
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: