Performance modeling for DFT algorithms in FFTW

Authors:
Liang Gu;Xiaoming Li
Affiliations:
University of Delaware, Newark, DE, USA;University of Delaware, Newark, DE, USA
Venue:
Proceedings of the 23rd international conference on Supercomputing
Year:
2009

Citing 1
Cited 0

Analysis of benchmark characteristics and benchmark performance prediction

ACM Transactions on Computer Systems (TOCS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Fast Fourier Transform in the West(FFTW) is one of the most successful adaptive Discrete Fourier Transform(DFT) libraries. The high-performance of FFTW mostly derives from its empirical search engine that includes all major DFT algorithms. We propose an adaptive model-driven FT performance prediction technique to replace the empirical search engine in FFTW. Our model achieves over 94% of the DFT performance and uses less than 5% of the search time compared with FFTW Exhaustive search on four test platforms.