Large-Scale Simulation of Quantum Computational Chemistry on a New Sunway Supercomputer

Shang, Honghui; Shen, Li; Fan, Yi; Zhao, Xu; Guo, Chu; Liu, Jie; Zhou, Wen‐Hao; Ma, Huan; Lin, Rongfen; Yang, Yibo; Li, Fang; Wang, Zhuoya; Zhang, Yunquan; Li, Zhenyu

doi:10.1109/sc41404.2022.00019

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…However, achieving optimal speedup is challenging, and only very few parallel implementations of quantum chemistry methods can demonstrate it. Domain decomposition and speculative parallelization are general techniques that have proven useful in identifying and designing parallel algorithms for large and complex quantum chemistry calculation tasks (Werner, 1995;González-Escribano et al, 2006;Su et al, 2007;Lipparini et al, 2013;Qiu et al, 2017;Nottoli et al, 2019;Sho and Odanaka, 2019;Jha et al, 2022;Shang et al, 2022;Fedorov and Pham, 2023).…”

Section: Scaling and Parallelization Of Quantum Chemistry Computationsmentioning

confidence: 99%

Coupled cluster theory on modern heterogeneous supercomputers

et al. 2023

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This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory—a linear-scaling, massively parallel framework—as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.

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Section: Scaling and Parallelization Of Quantum Chemistry Computationsmentioning

confidence: 99%

Coupled cluster theory on modern heterogeneous supercomputers

et al. 2023

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“…Meanwhile, the memory of a multi-node computer can be scaled to the petabytes order, but its bandwidth for access from host computers (CPUs) is narrow. To simultaneously accelerate simulations and enlarge the total memory space, the heterogeneous parallelization approach 15 (see sections "Heterogeneous parallelization strategy" and section "Julia programming language" for more details) can be adopted. Our simulator allocates memory to each computation node and then accelerates simulations by utilizing the full capabilities of the heterogeneous many-core processors.…”

Section: Optimization Strategiesmentioning

confidence: 99%

“…Here, we follow the strategy suggested in the qubit-ADAPT-VQE method 37 . While we did not iteratively build the wave function ansatz until convergence, high accuracy can be The above steps are performed by interfacing our MPS-VQE simulator with the Q 2 Chemistry package 15,36 . In this way, an approximate wave function ansatz that entangles every neighbouring 5 orbitals (10 qubits) is constructed for the hydrogen chain simulations.…”

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confidence: 99%

Towards practical and massively parallel quantum computing emulation for quantum chemistry

et al. 2023

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Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, the quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. However, existing simulators mostly suffer from the memory bottleneck so developing the approaches for large-scale quantum chemistry calculations remains challenging. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver (VQE) simulator based on matrix product states, combined with embedding theory for solving large-scale quantum computing emulation for quantum chemistry on HPC platforms. We apply this method to study the torsional barrier of ethane and the quantification of the protein–ligand interactions. Our largest simulation reaches 1000 qubits, and a performance of 216.9 PFLOP/s is achieved on a new Sunway supercomputer, which sets the state-of-the-art for quantum computing emulation for quantum chemistry.

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“…Hunold et al [24] used a Julia port of the STREAM benchmark [30] to show negligible overheads in the collective Bcast, Alltoall, and Allreduce MPI operations when compared against equivalent C implementations on up to 1,152 processes. Shang et al [40] used the Julia language ecosystem on the many-cores Sunway supercomputer to conduct quantum computational chemistry simulations. In their experiments they achieved up to 91% efficiency when measuring weak scaling on up to 21M cores.…”

Section: Related Workmentioning

confidence: 99%

Julia as a unifying end-to-end workflow language on the Frontier exascale system

Godoy,

Valero-Lara,

Anderson

et al. 2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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We evaluate Julia as a single language and ecosystem paradigm powered by LLVM to develop workflow components for high-performance computing. We run a Gray-Scott, 2variable diffusion-reaction application using a memory-bound, 7-point stencil kernel on Frontier, the US Department of Energy's first exascale supercomputer. We evaluate the performance, scaling, and trade-offs of (i) the computational kernel on AMD's MI250x GPUs, (ii) weak scaling up to 4,096 MPI processes/GPUs or 512 nodes, (iii) parallel I/O writes using the ADIOS2 library bindings, and (iv) Jupyter Notebooks for interactive analysis. Results suggest that although Julia generates a reasonable LLVM-IR, a nearly 50% performance difference exists vs. native AMD HIP stencil codes when running on the GPUs. As expected, we observed nearzero overhead when using MPI and parallel I/O bindings for system-wide installed implementations. Consequently, Julia emerges as a compelling high-performance and highproductivity workflow composition language, as measured on the fastest supercomputer in the world.

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Large-Scale Simulation of Quantum Computational Chemistry on a New Sunway Supercomputer

Cited by 12 publications

References 53 publications

Coupled cluster theory on modern heterogeneous supercomputers

Coupled cluster theory on modern heterogeneous supercomputers

Towards practical and massively parallel quantum computing emulation for quantum chemistry

Julia as a unifying end-to-end workflow language on the Frontier exascale system

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