Tensor Train Multiplication
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913566926307328 |
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| author | Michailidis, Alexios A Fenton, Christian Kiffner, Martin |
| author_facet | Michailidis, Alexios A Fenton, Christian Kiffner, Martin |
| contents | We present the Tensor Train Multiplication (TTM) algorithm for the elementwise multiplication of two tensor trains with bond dimension $χ$. The computational complexity and memory requirements of the TTM algorithm scale as $χ^3$ and $χ^2$, respectively. This represents a significant improvement compared with the conventional approach, where the computational complexity scales as $χ^4$ and memory requirements scale as $χ^3$.We benchmark the TTM algorithm using flows obtained from artificial turbulence generation and numerically demonstrate its improved runtime and memory scaling compared with the conventional approach. The TTM algorithm paves the way towards GPU accelerated tensor network simulations of computational fluid dynamics problems with large bond dimensions due to its dramatic improvement in memory scaling. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_19747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tensor Train Multiplication Michailidis, Alexios A Fenton, Christian Kiffner, Martin Computational Physics Quantum Physics We present the Tensor Train Multiplication (TTM) algorithm for the elementwise multiplication of two tensor trains with bond dimension $χ$. The computational complexity and memory requirements of the TTM algorithm scale as $χ^3$ and $χ^2$, respectively. This represents a significant improvement compared with the conventional approach, where the computational complexity scales as $χ^4$ and memory requirements scale as $χ^3$.We benchmark the TTM algorithm using flows obtained from artificial turbulence generation and numerically demonstrate its improved runtime and memory scaling compared with the conventional approach. The TTM algorithm paves the way towards GPU accelerated tensor network simulations of computational fluid dynamics problems with large bond dimensions due to its dramatic improvement in memory scaling. |
| title | Tensor Train Multiplication |
| topic | Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2410.19747 |