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Open AccessProceedings Article

A Parallel FFT on an MIMD Machine.

Amir Averbuch, +2 more
- pp 63-70
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TLDR
In this paper, a parallel Cooley-Tukey FFT implementation on a shared-memory MIMD (non-vector) machine that was built in the Dept. of Computer Science, Tel Aviv University is presented.
Abstract
Abstract In this paper we present a parallelization of the Cooley- Tukey FFT algorithm that is implemented on a shared-memory MIMD (non-vector) machine that was built in the Dept. of Computer Science, Tel Aviv University. A parallel algorithm is presented for one dimension Fourier transform with performance analysis. For a large array of complex numbers to be transformed, an almost linear speed-up is demonstrated. This algorithm can be executed by any number of processors, but generally the number is much less than the length of the input data.

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Citations
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Journal ArticleDOI

The scalability of FFT on parallel computers

TL;DR: The authors present the scalability analysis of a parallel fast Fourier transform (FFT) algorithm on mesh and hypercube connected multicomputers using the isoefficiency metric and show that it is more cost-effective to implement the FFT algorithm on a hypercube rather than a mesh.
Book ChapterDOI

FFTC: fastest Fourier transform for the IBM cell broadband engine

TL;DR: This paper uses an iterative out-of-place approach to design an efficient parallel implementation of FFT with 1K to 16K complex input samples and attain a single precision performance of 18.6 GFLOP/s on the Cell, believed to be the fastest FFT for this range of complex inputs.
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Computer vision algorithms on reconfigurable logic arrays

TL;DR: The use of the custom computing approach to meet the computation and communication needs of computer vision algorithms by customizing hardware architecture at the instruction level for every application so that the optimal grain size needed for the problem at hand and the instruction granularity can be matched.
Proceedings ArticleDOI

Optimizing the Fast Fourier Transform on a Multi-core Architecture

TL;DR: It is demonstrated that successful optimization for C64-like large-scale multi-core architectures requires a careful analysis that can identify certain domain-specific features of a target application and match them well with some key multi- core architecture features.
Proceedings ArticleDOI

A high performance parallel algorithm for 1-D FFT

TL;DR: It is shown that the multi-dimensional formulation of the proposed FFT algorithm helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node.
References
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An algorithm for the machine calculation of complex Fourier series

TL;DR: Good generalized these methods and gave elegant algorithms for which one class of applications is the calculation of Fourier series, applicable to certain problems in which one must multiply an N-vector by an N X N matrix which can be factored into m sparse matrices.
MonographDOI

Numerical analysis of spectral methods : theory and applications

TL;DR: Spectral Methods Survey of Approximation Theory Review of Convergence Theory Algebraic Stability Spectral Methods Using Fourier Series Applications of algebraic stability analysis Constant Coefficient Hyperbolic Equations Time Differencing Efficient Implementation of Spectral Method as discussed by the authors.
Book

Computer Architecture and Parallel Processing

Kai Hwang, +1 more
TL;DR: The authors have divided the use of computers into the following four levels of sophistication: data processing, information processing, knowledge processing, and intelligence processing.
Journal ArticleDOI

A Fast Direct Solution of Poisson's Equation Using Fourier Analysis

TL;DR: This work has developed a direct method of solution involving Fourier analysis which can solve Poisson''s equation in a square region covered by a 48 x 48 mesh in 0.9 seconds on the IBM 7090.
Journal ArticleDOI

The Indirect Binary n-Cube Microprocessor Array

TL;DR: This paper explores the possibility of using a large-scale array of microprocessors as a computational facility for the execution of massive numerical computations with a high degree of parallelism.
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