Computational Workloads for Commonly Used Signal Processing Kernels
Abstract
In the course of designing or evaluating signal processing algorithms, one often must determine the computational workload needed to implement the algorithms on a digital computer. The floating-point operation (flop) counts for real versions of the most common signal processing kernels are well documented. However, the flop counts for kernels operating on complex inputs are not as readily found. This report collects the flop count expressions for both real and complex kernels and also presents brief outlines of the derivations for the flop count expressions. Specifically, the following computational kernels are addressed: (1) the dimensions of the two multiplicands (m x n and n x p) for the matrix-matrix multiplication; (2) the length of the vector n for the fast Fourier transform; (3) the size of the triangular system n for forward and back substitutions; (4) the dimensions of the input matrix m x n for the Householder QR decomposition, eigenvalue decomposition, and singular value decomposition.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 30, 2006
- Accession Number
- ADA458534
Entities
People
- M. Arakawa
Organizations
- Massachusetts Institute of Technology