Machine‐learning assisted scheduling optimization and its application in quantum chemical calculations

Ma, Yingjin; Li, Zhiying; Chen, Xin; Ding, Bowen; Li, Ning; Lu, Teng; Zhang, Baohua; Suo, Bingbing

doi:10.1002/jcc.27075

Cited by 5 publications

(2 citation statements)

References 111 publications

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“…Load balancing aims to distribute the computational workload evenly among multiple processors or nodes to reduce idle time and communication overhead. This distribution of the workload enhances the performance, efficiency, and scalability of parallel quantum chemistry applications ( Nikodem et al, 2014 ; Ma et al, 2023 ). Thus, the proper use of the load balancing technique is essential for many production codes and calculations.…”

Section: Scaling and Parallelization Of Quantum Chemistry Computationsmentioning

confidence: 99%

“…Algorithm 1 ; Algorithm 2 provide a scheme that enables the calculation of CPS(D-3) excitation energies for system sizes beyond the reach of conventional CCSD calculations. To perform all the costly tensor contractions in each batch, these algorithms use the Tensor Algebra Library for Shared Memory Computers (TALSH) ( Lyakh, 2023 ), which transfers them to GPUs. Open Multiprocessing (OMP) allows each rank to exploit shared memory parallelism locally.…”

Section: Cluster Perturbation Theorymentioning

confidence: 99%

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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%

Section: Cluster Perturbation Theorymentioning

confidence: 99%

Coupled cluster theory on modern heterogeneous supercomputers

et al. 2023

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Fault‐tolerant quantum chemical calculations with improved machine‐learning models

Yuan,

Zhou,

et al. 2024

J Comput Chem

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Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine‐learning (ML) assisted scheduling optimization [J. Comput. Chem. 2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load‐balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re‐normalized exciton model with time‐dependent density functional theory (REM‐TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML‐assisted coded calculations can further improve the load‐balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states.

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Multi-level parallelization of quantum-chemical calculations

Fedorov

Pham

2023

The Journal of Chemical Physics

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Strategies for multiple-level parallelizations of quantum-mechanical calculations are discussed, with an emphasis on using groups of workers for performing parallel tasks. These parallel programming models can be used for a variety ab initio quantum chemistry approaches, including the fragment molecular orbital method and replica-exchange molecular dynamics. Strategies for efficient load balancing on problems of increasing granularity are introduced and discussed. A 4-level parallelization is developed based on a multi-level hierarchical grouping, and a high parallel efficiency is achieved on the Theta supercomputer using 131,072 OpenMP threads.

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Machine‐learning assisted scheduling optimization and its application in quantum chemical calculations

Cited by 5 publications

References 111 publications

Coupled cluster theory on modern heterogeneous supercomputers

Coupled cluster theory on modern heterogeneous supercomputers

Fault‐tolerant quantum chemical calculations with improved machine‐learning models

Multi-level parallelization of quantum-chemical calculations

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