Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version)
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913571570450432 |
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| author | Xu, Mingkuan Cao, Shiyi Miao, Xupeng Acar, Umut A. Jia, Zhihao |
| author_facet | Xu, Mingkuan Cao, Shiyi Miao, Xupeng Acar, Umut A. Jia, Zhihao |
| contents | This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2$\times$ on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version) Xu, Mingkuan Cao, Shiyi Miao, Xupeng Acar, Umut A. Jia, Zhihao Distributed, Parallel, and Cluster Computing Quantum Physics This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2$\times$ on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude. |
| title | Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version) |
| topic | Distributed, Parallel, and Cluster Computing Quantum Physics |
| url | https://arxiv.org/abs/2408.09055 |