Skew-Symmetric Matrix Decompositions on Shared-Memory Architectures
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| Julkaisussa: | arXiv.org (Nov 15, 2024), p. n/a |
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| Päätekijä: | |
| Muut tekijät: | , , |
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Cornell University Library, arXiv.org
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| Linkit: | Citation/Abstract Full text outside of ProQuest |
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|---|---|---|---|
| 001 | 3129864273 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3129864273 | ||
| 045 | 0 | |b d20241115 | |
| 100 | 1 | |a Satyarth, Ishna | |
| 245 | 1 | |a Skew-Symmetric Matrix Decompositions on Shared-Memory Architectures | |
| 260 | |b Cornell University Library, arXiv.org |c Nov 15, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a The factorization of skew-symmetric matrices is a critically understudied area of dense linear algebra (DLA), particularly in comparison to that of symmetric matrices. While some algorithms can be adapted from the symmetric case, the cost of algorithms can be reduced by exploiting skew-symmetry. A motivating example is the factorization \(X=LTL^T\) of a skew-symmetric matrix \(X\), which is used in practical applications as a means of determining the determinant of \(X\) as the square of the (cheaply-computed) Pfaffian of the skew-symmetric tridiagonal matrix \(T\), for example in fields such as quantum electronic structure and machine learning. Such applications also often require pivoting in order to improve numerical stability. In this work we explore a combination of known literature algorithms and new algorithms recently derived using formal methods. High-performance parallel CPU implementations are created, leveraging the concept of fusion at multiple levels in order to reduce memory traffic overhead, as well as the BLIS framework which provides high-performance GEMM kernels, hierarchical parallelism, and cache blocking. We find that operation fusion and improved use of available bandwidth via parallelization of bandwidth-bound (level-2 BLAS) operations are essential for obtaining high performance, while a concise C++ implementation provides a clear and close connection to the formal derivation process without sacrificing performance. | |
| 653 | |a Parallel processing | ||
| 653 | |a Computer memory | ||
| 653 | |a Linear algebra | ||
| 653 | |a Matrices (mathematics) | ||
| 653 | |a Formal method | ||
| 653 | |a Algorithms | ||
| 653 | |a Numerical stability | ||
| 653 | |a Machine learning | ||
| 653 | |a Determinants | ||
| 653 | |a Electronic structure | ||
| 653 | |a Symmetry | ||
| 653 | |a Factorization | ||
| 700 | 1 | |a Yin, Chao | |
| 700 | 1 | |a Xu, RuQing G | |
| 700 | 1 | |a Matthews, Devin A | |
| 773 | 0 | |t arXiv.org |g (Nov 15, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3129864273/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2411.09859 |