Crystal-KMC: parallel software for lattice dynamics monte carlo simulation of metal materials

Li, Jianjiang; Peng, Wei; Yang, Shi‐Xia; Wu, Jie; Liu, Peng; He, Xinfu

doi:10.26599/tst.2018.9010107

Cited by 11 publications

(13 citation statements)

References 10 publications

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“…2. In the previous work, the MPI communication of Crystal-KMC software has been optimized [13] . Therefore, we focus on the optimization of computing transition module and the energy update module.…”

Section: Performance Bottleneck Analysismentioning

confidence: 99%

“…To meet the needs of simulating the specific environment of irradiation damage, our research group has independently developed a parallel KMC simulation software called Crystal-KMC [13] . The parallel software is based on lattice dynamics; it supports EAM potential functions and takes full advantage of the characteristics of data sharing in shared memory.…”

Section: Introductionmentioning

confidence: 99%

“…The Crystal-KMC, a KMC parallel software developed by our group, is mainly used to simulate the aging of nuclear cladding materials under fast neutron irradiation. The software supports the EAM potential function, customizes radiation damage of nuclear materials, and innovatively proposes an optimization method to reduce the communication overhead [13] . Figure 1 shows the basic process of Crystal-KMC performing transition simulation within a certain time threshold.…”

Section: Introduction To Crystal-kmcmentioning

confidence: 99%

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Parallel optimization of the Crystal-KMC on Tianhe-2

Yang

et al. 2021

Tinshhua Sci. Technol.

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The Kinetic Monte Carlo (KMC) is one of the commonly used methods for simulating radiation damage of materials. Our team develops a parallel KMC software named Crystal-KMC, which supports the Embedded Atom Method (EAM) potential energy and utilizes the Message Passing Interface (MPI) technology to simulate the vacancy transition of the Copper (Cu) element under neutron radiation. To make better use of the computing power of modern supercomputers, we develop the parallel efficiency optimization model for the Crystal-KMC on Tianhe-2, to achieve a larger simulation of the damage process of materials under irradiation environment. Firstly, we analyze the performance bottleneck of the Crystal-KMC software and use the MIC offload statement to implement the operation of key modules of the software on the MIC coprocessor. We use OpenMP to develop parallel optimization for the Crystal-KMC, combined with existing MPI inter-process communication optimization, finally achieving hybrid parallel optimization. The experimental results show that in the single-node CPU and MIC collaborative parallel mode, the speedup of the calculation hotspot reaches 30.1, and the speedup of the overall software reaches 7.43.

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Section: Performance Bottleneck Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction To Crystal-kmcmentioning

confidence: 99%

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Parallel optimization of the Crystal-KMC on Tianhe-2

Yang

et al. 2021

Tinshhua Sci. Technol.

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“…The single-physics simulation software in the CVR includes newly developed software programs and legacy ones. For examples, CVR-PASA and ANT-MOC are newly developed in the CVR project, and material performance simulation software molecular dynamics software programs CrystalMD 92,93,95 and Kinetic Monte Carlo software CrystalKMC 94 are legacy software programs.…”

Section: Multi-physics Coupling In China Virtualmentioning

confidence: 99%

Multi-physics coupling simulation in virtual reactors

Wang

et al. 2019

SIMULATION

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Nuclear power stations involve a range of complex and interacting multi-physical processes. With the rapid development of high-performance computing technology, accurate multi-physics simulation in virtual reactors has drawn more and more attention in industry and academia. Great efforts have been made toward the simulation of multi-physics coupling processes in reactors. The interpretations of many terms that describe multi-physics simulation vary in different literatures. We organize and discuss some important terms relevant to the multi-physics coupling simulation. We compare the three most frequently used multi-physics coupling strategies: the operator splitting, Picard iteration, and Jacobian-Free Newton–Krylov methods. We summarize three main viewpoints on the degree of coupling of the three strategies (loose, tight, or full coupling). Then we review the coupling software and corresponding coupling strategies in some representative virtual reactor projects. We present the research focuses of Spider coupling platform. The Spider is developed in the China Virtual Reactor (CVR) project. The multi-physics phenomena are considered in the CVR project from three scales: fuel scale, reactor core scale, and system scale. Both loose and tight coupling strategies are supported in the Spider platform.

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“…Tere are many existing problem transformation methods, such as the BR method [9], the CC theory [10], the MBR model [11], and the DLMC-OS algorithm [12]. However, these methods usually ignore the correlation among labels, the randomness of label sequences, and the redundant interactive label information, which reduces the accuracy of classifcation.…”

Section: Introductionmentioning

confidence: 99%

Improved Double‐Layer Structure Multilabel Classification Model via Optimal Sequence and Attention Mechanism

Liu

Tan

2022

Complexity

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Multilabel classification is a key research topic in the machine learning field. In this study, the author put forward a two/two-layer chain classification algorithm with optimal sequence based on the attention mechanism. This algorithm is a classification model with a two-layer structure. By introducing an attention mechanism, this study analyzes the key attributes to achieve the goal of classification. To solve the problem of algorithm accuracy degradation caused by the order of classifiers, we adopt the OSS (optimal sequence selection) algorithm to find the optimal sequence of tags. The test results based on the actual dataset show that the ATDCC-OS algorithm has good performance on all performance evaluation metrics. The average accuracy of this algorithm is over 80%. The microaverage AUC performance reaches 0.96. In terms of coverage performance, its coverage performance is below 10%. The comprehensive result of single error performance is the best. The loss performance is about 0.03. The purpose of the ATDCC-OS algorithm proposed in the study is to help improve the accuracy of multilabel classification so as to obtain more effective data information.

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Crystal-KMC: parallel software for lattice dynamics monte carlo simulation of metal materials

Cited by 11 publications

References 10 publications

Parallel optimization of the Crystal-KMC on Tianhe-2

Parallel optimization of the Crystal-KMC on Tianhe-2

Multi-physics coupling simulation in virtual reactors

Improved Double‐Layer Structure Multilabel Classification Model via Optimal Sequence and Attention Mechanism

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