(1 + ε)-approximate sparse recovery
Eric Price, David P. Woodruff
FOCS 2011
FFTW is an implementation of the discrete Fourier transform (DFT) that adapts to the hardware in order to maximize performance. This paper shows that such an approach can yield an implementation that is competitive with hand-optimized libraries, and describes the software structure that makes our current FFTW3 version flexible and adaptive. We further discuss a new algorithm for real-data DFTs of prime size, a new way of implementing DFTs by means of machine-specific single-instruction, multiple-data (SIMD) instructions, and how a special-purpose compiler can derive optimized implementations of the discrete cosine and sine transforms automatically from a DFT algorithm. © 2005 IEEE.
Eric Price, David P. Woodruff
FOCS 2011
Michael Ray, Yves C. Martin
Proceedings of SPIE - The International Society for Optical Engineering
Rajiv Ramaswami, Kumar N. Sivarajan
IEEE/ACM Transactions on Networking
N.K. Ratha, A.K. Jain, et al.
Workshop CAMP 2000