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Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures
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DOI：10.1021/acs.jctc.4c00661.png)
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摘要
En 中文
The interplay of quantum and classical simulation and the delicate divide between them is in the focus of massively parallelized tensor network state (TNS) algorithms designed for high performance computing (HPC). In this contribution, we present novel algorithmic solutions together with implementation details to extend current limits of TNS algorithms on HPC infrastructure building on state-of-the-art hardware and software technologies. Benchmark results obtained via large-scale density matrix renormalization group (DMRG) simulations on single node multiGPU NVIDIA A100 system are presented for selected strongly correlated molecular systems addressing problems on Hilbert space dimensions up to 4.17 x 1035.
Keyword:
MATRIX RENORMALIZATION-GROUP
QUANTUM-CHEMISTRY
PRODUCT STATES
PERFORMANCE
IMPLEMENTATION
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期刊
IF:
5.5
论文数:
1.1W
被引数:
5.4W
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