Operators for Data Redistribution: Applications to the STL library and RayTracing Algorithm

Implementations of various fast Fourier transform (FFT) algorithms are presented for distributed-memory multiprocessors. These algorithms use data redistribution to localize the computation. The goal is to optimize communication cost by using a minimum number of redistribution steps. Both analytical and experimental performance results on the Intel iPSC/860 system are presented.

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Mapping functions and data redistribution for parallel files

The Journal of Supercomputing ◽

10.1007/s11227-007-0165-x ◽

2008 ◽

Vol 46 (3) ◽

pp. 213-236 ◽

Cited By ~ 1

Author(s):

Florin Isaila ◽

Walter F. Tichy

Keyword(s):

Mapping Functions ◽

Data Redistribution

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Efficient block cyclic data redistribution

Lecture Notes in Computer Science - Euro-Par'96 Parallel Processing ◽

10.1007/3-540-61626-8_20 ◽

1996 ◽

pp. 155-164 ◽

Cited By ~ 10

Author(s):

Loïc Prylli ◽

Bernard Tourancheau

Keyword(s):

Data Redistribution ◽

Cyclic Data

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MADRE: The Memory-Aware Data Redistribution Engine

Recent Advances in Parallel Virtual Machine and Message Passing Interface - Lecture Notes in Computer Science ◽

10.1007/978-3-540-87475-1_31 ◽

2008 ◽

pp. 218-226 ◽

Cited By ~ 11

Author(s):

Stephen F. Siegel ◽

Andrew R. Siegel

Keyword(s):

Data Redistribution

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The fast Fourier transform

Parallel Scientific Computation ◽

10.1093/oso/9780198788348.003.0003 ◽

2020 ◽

pp. 134-189

Author(s):

Rob H. Bisseling

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Weather Forecasting ◽

Data Access ◽

Regular Pattern ◽

Matrix Products ◽

Data Redistribution ◽

Fft Algorithms ◽

Matrix Vector

This chapter demonstrates the use of different data distributions in different phases of a parallel fast Fourier transform (FFT), which is a regular computation with a predictable but challenging data access pattern. Both the block and cyclic distributions are used and also intermediates between them. Each required data redistribution is a permutation that involves communication. By making careful choices, the number of such redistributions can be kept to a minimum. FFT algorithms can be concisely expressed using matrix/vector notation and Kronecker matrix products. This notation is also used here. The chapter then shows how permutations with a regular pattern can be implemented more efficiently by packing the data. The parallelization techniques discussed for the specific case of the FFT are also applicable to other related computations, for instance in signal processing and weather forecasting.

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