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Diamond matrix powers kernels

Published: 15 January 2020 Publication History
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    Matrix powers kernel calculates the vectors Akv, for k = 1, 2,..., m and they are the heart of various scientific computations, including communication avoiding iterative solvers. In this paper we propose diamond matrix powers kernel - DMPK, which has the purpose to apply the "diamond tiling" stencil algorithm to general matrices. It can also be considered as an extension of the PA1 and PA2 algorithms, introduced by Demmel et al. Our approach enables us to control the balance between the amount of communication avoidance and redundant computation inherently present in communication avoiding algorithms. We present a proof of concept implementation of the algorithm using MPI routines. The experiments we performed show that the control of the amount of computation and communication is achievable, and with more thorough optimisations, DMPK is a promising alternative to existing MPK approaches.

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    Mark Hoemmen. 2010. Communication-avoiding Krylov subspace methods. Ph.D. Dissertation. University of California, Berkeley.
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    Reiji Suda. 2016. Domain Decomposition Algorithm for Generalized Diamond Matrix Powers Kernel. In IPSJ SIG Technical Report.
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    David G. Wonnacott and Michelle Mills Strout. 2013. On the Scalability of Loop Tiling Techniques. In Proc. IMPACT 2013.
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    • (2023)Level-Based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector MultiplicationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.322351234:2(581-597)Online publication date: 1-Feb-2023

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    cover image ACM Other conferences
    HPCAsia '20: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region
    January 2020
    247 pages
    ISBN:9781450372367
    DOI:10.1145/3368474
    © 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 15 January 2020

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    • (2023)Level-Based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector MultiplicationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.322351234:2(581-597)Online publication date: 1-Feb-2023

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