<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Practical realizations of 3D forward/inverse separable discrete transforms, such as Fourier transform, cosine/sine transform, etc. are frequently the principal limiters that prevent many practical applications from scaling to a large number of processors. Existing approaches, which are based primarily on 1D or 2D data decompositions, prevent the 3D transforms from effectively scaling to the maximum (possible/available) number of computer nodes. A highly scalable approach to realize forward/inverse 3D transforms has been proposed. It is based on a 3D decomposition of data and geared towards a torus network of computer nodes. The proposed algorithms requires compute-and-roll time-steps, where each step consists of an execution of multiple GEMM operations and concurrent movement of cubical data blocks between nearest neighbors. The aim of this paper is to present an experimental performance study of an implementation on high performance computer architecture.</p></abstract>

Practical realizations of 3D forward/inverse separable discrete transforms, such as Fourier transform, cosine/sine transform, etc. are frequently the principal limiters that prevent many practical applications from scaling to a large number of processors. Existing approaches, which are based primarily on 1D or 2D data decompositions, prevent the 3D transforms from effectively scaling to the maximum (possible/available) number of computer nodes. A highly scalable approach to realize forward/inverse 3D transforms has been proposed. It is based on a 3D decomposition of data and geared towards a torus network of computer nodes. The proposed algorithms requires compute-and-roll time-steps, where each step consists of an execution of multiple GEMM operations and concurrent movement of cubical data blocks between nearest neighbors. The aim of this paper is to present an experimental performance study of an implementation on high performance computer architecture.

Performance Analysis of a Scalable Algorithm for 3D Linear Transforms on Supercomputer with Intel Processors/Co-Processors

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Practical realizations of 3D forward/inverse separable discrete transforms, such as Fourier transform, cosine/sine transform, etc. are frequently the principal...

Performance Analysis of a Scalable Algorithm for 3D Linear Transforms on Supercomputer with Intel Processors/Co-Processors

Published Online: Dec 31, 2020

Page range: 94 - 104

Received: Sep 15, 2020

Accepted: Oct 23, 2020

DOI: https://doi.org/10.2478/cait-2020-0064

Keywords
3D linear transforms, parallel implementation, Intel processors/ co-processors

© 2020 Ivan Lirkov, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Performance Analysis of a Scalable Algorithm for 3D Linear Transforms on Supercomputer with Intel Processors/Co-Processors

Published Online: Dec 31, 2020

Page range: 94 - 104

Received: Sep 15, 2020

Accepted: Oct 23, 2020

DOI: https://doi.org/10.2478/cait-2020-0064

Keywords3D linear transforms, parallel implementation, Intel processors/ co-processors

© 2020 Ivan Lirkov, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
3D linear transforms, parallel implementation, Intel processors/ co-processors