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Publications of SPCL

G. Kwasniewski, M. Kabić, T. Ben-Nun, A. Nikolaos Ziogas, J. Eirik Saethre, A. Gaillard, T. Schneider, M. Besta, A. Kozhevnikov, J. VandeVondele, T. Hoefler:

 On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal Matrix Factorizations

(In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21), Nov. 2021, )

Publisher Reference

Abstract

Matrix factorizations are among the most important building blocks of scientific computing. State-of-the-art libraries, however, are not communication-optimal, underutilizing current parallel architectures. We present novel algorithms for Cholesky and LU factorizations that utilize an asymptotically communication-optimal 2.5D decomposition. We first establish a theoretical framework for deriving parallel I/O lower bounds for linear algebra kernels, and then utilize its insights to derive Cholesky and LU schedules, both communicating N^3/(P*sqrt(M)) elements per processor, where M is the local memory size. The empirical results match our theoretical analysis: our implementations communicate significantly less than Intel MKL, SLATE, and the asymptotically communication-optimal CANDMC and CAPITAL libraries. Our code outperforms these state-of-the-art libraries in almost all tested scenarios, with matrix sizes ranging from 2,048 to 262,144 on up to 512 CPU nodes of the Piz Daint supercomputer, decreasing the time-to-solution by up to three times. Our code is ScaLAPACK-compatible and available as an open-source library.

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BibTeX

@inproceedings{,
  author={Grzegorz Kwasniewski and Marko Kabić and Tal Ben-Nun and Alexandros Nikolaos Ziogas and Jens Eirik Saethre and André Gaillard and Timo Schneider and Maciej Besta and Anton Kozhevnikov and Joost VandeVondele and Torsten Hoefler},
  title={{On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal Matrix Factorizations}},
  year={2021},
  month={11},
  booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21)},
  note={},
}