FastMPS: Revisit Data Parallel in Large-scale Matrix Product State Sampling
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908730227949568 |
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| author | Chen, Yaojian Gong, Si-Qiu Gan, Lin Liu, Yanfei Yang, An Wang, Yinuo Lu, Chao-yang Yang, Guangwen |
| author_facet | Chen, Yaojian Gong, Si-Qiu Gan, Lin Liu, Yanfei Yang, An Wang, Yinuo Lu, Chao-yang Yang, Guangwen |
| contents | Matrix Product State (MPS) is a versatile tensor network representation widely applied in quantum physics, quantum chemistry, and machine learning, etc. MPS sampling serves as a critical fundamental operation in these fields. As the problems become more complex, the scale of MPS is rapidly increasing. Traditional data parallelism is limited by memory and heavy I/O in large-scale MPS. Model parallelism that can handle large-scale MPS imposes rigid process bindings and lacks scalability. This work proposes Fast-MPS, a multi-level parallel framework for scalable MPS sampling. Our design combines data parallelism across samples with tensor parallelism along bond dimensions. We eliminate memory and I/O pressure through compression and overlapping, and revive data parallel in large-scale MPS sampling. We evaluate our approach on Gaussian Boson Sampling, a representative and demanding application. Fast-MPS achieves over 10x speedup compared to existing simulators, scales to thousands of processes, and enables simulations with 8,176 sites and bond dimension chi = 10^4, significantly outperforming the state of the art. Fast-MPS has demonstrated great potential in high-performance tensor network applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20064 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FastMPS: Revisit Data Parallel in Large-scale Matrix Product State Sampling Chen, Yaojian Gong, Si-Qiu Gan, Lin Liu, Yanfei Yang, An Wang, Yinuo Lu, Chao-yang Yang, Guangwen Distributed, Parallel, and Cluster Computing J.2 Matrix Product State (MPS) is a versatile tensor network representation widely applied in quantum physics, quantum chemistry, and machine learning, etc. MPS sampling serves as a critical fundamental operation in these fields. As the problems become more complex, the scale of MPS is rapidly increasing. Traditional data parallelism is limited by memory and heavy I/O in large-scale MPS. Model parallelism that can handle large-scale MPS imposes rigid process bindings and lacks scalability. This work proposes Fast-MPS, a multi-level parallel framework for scalable MPS sampling. Our design combines data parallelism across samples with tensor parallelism along bond dimensions. We eliminate memory and I/O pressure through compression and overlapping, and revive data parallel in large-scale MPS sampling. We evaluate our approach on Gaussian Boson Sampling, a representative and demanding application. Fast-MPS achieves over 10x speedup compared to existing simulators, scales to thousands of processes, and enables simulations with 8,176 sites and bond dimension chi = 10^4, significantly outperforming the state of the art. Fast-MPS has demonstrated great potential in high-performance tensor network applications. |
| title | FastMPS: Revisit Data Parallel in Large-scale Matrix Product State Sampling |
| topic | Distributed, Parallel, and Cluster Computing J.2 |
| url | https://arxiv.org/abs/2512.20064 |