Implementations of the FFT algorithm on GPU

Sreehari Ambuluri
Department of Electrical Engineering, Linkopings universitet, SE-581 83 Linkoping, Sweden
Linkopings universitet, 2013

   title={Implementations of the FFT algorithm on GPU},

   author={Ambuluri, Sreehari},



Download Download (PDF)   View View   Source Source   



The fast Fourier transform (FFT) plays an important role in digital signal processing (DSP) applications, and its implementation involves a large number of computations. Many DSP designers have been working on implementations of the FFT algorithms on different devices, such as central processing unit (CPU), Field programmable gate array (FPGA), and graphical processing unit (GPU), in order to accelerate the performance. We selected the GPU device for the implementations of the FFT algorithm because the hardware of GPU is designed with highly parallel structure. It consists of many hundreds of small parallel processing units. The programming of such a parallel device, can be done by a parallel programming language CUDA (Compute Unified Device Architecture). In this thesis, we propose different implementations of the FFT algorithm on the NVIDIA GPU using CUDA programming language. We study and analyze the different approaches, and use different techniques to accelerate the computations of the FFT. We also discuss the results and compare different approaches and techniques. Finally, we compare our best cases of results with the CUFFT library, which is a specific library to compute the FFT on NVIDIA GPUs.
VN:F [1.9.22_1171]
Rating: 5.0/5 (1 vote cast)
Implementations of the FFT algorithm on GPU, 5.0 out of 5 based on 1 rating

* * *

* * *

Follow us on Twitter

HGPU group

1543 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

274 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: