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An efficient parallel algorithm for 3D magnetotelluric modeling with edge-based finite element

COMPUTATIONAL GEOSCIENCES 2021年第1期25卷 1-16页

作者： Zhu, Xiaoxiong Liu, Jie Cui, Yian Gong, Chunye Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Cent South Univ Sch Geosci & Infophys Changsha 410083 Peoples R China

Three-dimensional magnetotelluric modeling algorithm of high accuracy and high efficiency is required for data interpretation and inversion. In this paper, edge-based finite element method with unstructured mesh is used to solve 3D magnetotelluric problem. Two boundary conditions-Dirichlet boundary condition and Neumann boundary condition-are set for cross-validation and comparison. We propose an efficient parallel algorithm to speed up computation and improve efficiency. The algorithm is based on distributed matrix storage and has three levels of parallelism. The first two are process level parallelization for frequencies and matrix solving, and the last is thread-level parallelization for loop unrolling. The algorithm is validated by several model studies. Scalability tests have been performed on two distributed-memory HPC platforms, one consists of IntelE5-2660 microprocessors and the other consists of Phytium FT2000 Plus microprocessors. On Intel platform, computation time of our algorithm solving Dublin Test Model-1 with 3,756,373 edges at 21 frequencies is 365 s on 2520 cores. The speedup and efficiency are 1609 and 60% compared to 100 cores. On Phytium platform, scalability test shows that the speedup from 256 cores to 86,016 cores has been increased to 11,255.

关键词： Magnetotelluric Edge-based finite element Unstructured mesh parallel algorithm

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An efficient heterogeneous parallel algorithm of the 3D MOC for multizone heterogeneous systems

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COMPUTER PHYSICS COMMUNICATIONS 2023年第1期292卷

作者： Li, Runhua Liu, Jie Zhang, Guangchun Gong, Chunye Yang, Bo Liang, Yuechao Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

Recent innovations in powerful high-performance computing (HPC) architecture have enabled high-fidelity whole-core neutron transport simulations in a reasonable amount of time. Among high-fidelity numerical methods, the one-step 3D Method of Characteristic (MOC) is becoming increasingly popular in the reactor design community due to its numerical accuracy, geometrical flexibility and high degrees of parallelism. In this work, we propose a sophisticated heterogeneous parallel algorithm of the 3D MOC based on a supercomputer prototype consisting of brand-new multizone heterogeneous processors called the MT-3000, which has a peak double precision performance of 11.6 TFLOPS when operating at 1.2 GHz. In the 3D MOC calculation procedure, the total run time is dominated by the so-called MOC transport sweep where large amounts of tracks traverse the mesh grids along different directions. We significantly improve the performance of the MOC transport sweep through a three-level parallel algorithm with multiple hiding latency techniques. We have also implemented heterogeneous level-1 basic linear algebra subprograms (BLAS) that's the second computational hot spot. The 3D C5G7 benchmark problem is employed to verify the correctness and efficiency of the heterogeneous parallel 3D MOC algorithm. Numerical results show that it achieves very high performance on the MT-3000-based supercomputer prototype. Specifically, the three-layer parallel algorithm has proven to be highly scalable for each layer, and the Quarter-MT-3000 performance of the MOC transport sweep has achieved 23.6% of the peak performance, which is close to the theoretical upper limit.& COPY;2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

关键词： Method of characteristics Neutron transport Reactor simulation Heterogeneous parallel High performance computing MT-3000

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wu, Hongzhou Lyu, Yifan Shen, Xingyu Zhao, Xuechen Wang, Mengzhu Zhang, Xiang Luo, Zhigang Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha Peoples R China Univ Chinese Acad Sci Inst Software Chinese Acad Sci Beijing Peoples R China Natl Univ Def Technol Sch Comp Changsha Peoples R China Natl Univ Def Technol Inst Quantum Informat Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China

ISBN: (纸本)9781665468916

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

关键词： weakly supervised temporal language grounding cross-modal interaction contrastive learning atomic action discriminative word

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Enhancing Graph Convolutional Networks by Topology Sampling

Enhancing Graph Convolutional Networks by Topology Sampling

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Petit, Quentin R. Li, Chong Petiton, Serge G. Chai, Kelun Emad, Nahid Huawei Paris Research Center Université Paris-Saclay Boulogne-Billancourt France Huawei Paris Research Center Distributed and Parallel Software Lab Boulogne-Billancourt France Université de Lille Lille France Université Versailles Saint-Quentin Maison de la Simulation & LI-PaRAD Saclay France

ISBN: (纸本)9781665480451

Graph Neural Networks (GNNs) play a very important role today. It does analyze not only the graph data itself, but also the data connectivity of the graph. The quality of a GNN is thus altered by the result of extracted graph structure information. The extraction could be enhanced by GNN model design or directly from the training dataset with a GNN-decoupled method. In this paper, we propose RankedDrop, a new sampling method to improve the extraction of graph structure information. This approach is based on droppingout technique, and it adopts a spatial-aware selection of edges to drop. It takes into account structure information of the graph to control the dropping-out, and its random selection of edges to be dropped is under the control of a probability generated with respect to graph's topological importance. Our experiments point out that RankedDrop provides high-quality and robust training results compared to the leading solutions. Furthermore, RankedDrop could be a framework plugin and combined with GNN model improvements to maximize GNN quality. Furthermore, RankedDrop could be a plugin for AI frameworks like MindSpore and combined with GNN model improvements to maximize GNN quality. © 2022 IEEE.

关键词： Network topology

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Variational Distillation for Multi-View Learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第7期46卷 4551-4566页

作者： Tian, Xudong Zhang, Zhizhong Wang, Cong Zhang, Wensheng Qu, Yanyun Ma, Lizhuang Wu, Zongze Xie, Yuan Tao, Dacheng East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Huawei Technol Distributed & Parallel Software Lab Labs 2012 Hangzhou 518129 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Xiamen Univ Sch Informat Sci & Technol Xiamen 361005 Fujian Peoples R China East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200050 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shenzhen Univ Coll Mech & Control Engn Shenzhen 518060 Peoples R China Normal Univ Chongqing Inst East China Shanghai 200062 Peoples R China JD Exploer Acad Beijing Peoples R China Univ Sydney Camperdown NSW 2050 Australia

Information Bottleneck (IB) provides an information-theoretic principle for multi-view learning by revealing the various components contained in each viewpoint. This highlights the necessity to capture their distinct roles to achieve view-invariance and predictive representations but remains under-explored due to the technical intractability of modeling and organizing innumerable mutual information (MI) terms. Recent studies show that sufficiency and consistency play such key roles in multi-view representation learning, and could be preserved via a variational distillation framework. But when it generalizes to arbitrary viewpoints, such strategy fails as the mutual information terms of consistency become complicated. This paper presents Multi-View Variational Distillation ((MVD)-D-2), tackling the above limitations for generalized multi-view learning. Uniquely, (MVD)-D-2 can recognize useful consistent information and prioritize diverse components by their generalization ability. This guides an analytical and scalable solution to achieving both sufficiency and consistency. Additionally, by rigorously reformulating the IB objective, (MVD)-D-2 tackles the difficulties in MI optimization and fully realizes the theoretical advantages of the information bottleneck principle. We extensively evaluate our model on diverse tasks to verify its effectiveness, where the considerable gains provide key insights into achieving generalized multi-view representations under a rigorous information-theoretic principle.

关键词： Mutual information Task analysis Representation learning Predictive models Optimization Visualization Pattern analysis Multi-view learning information bottleneck mutual information variational inference knowledge distillation

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An Open Source Graph-Based Weighted Cycle Closure Method for Relative Binding Free Energy Calculations

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2022年第2期63卷 561-570页

作者： Li, Yishui Liu, Runduo Liu, Jie Luo, Haibin Wu, Chengkun Li, Zhe Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China Sun Yat Sen Univ Sch Pharmaceut Sci Guangzhou 510275 Guangdong Peoples R China Hainan Univ Sch Pharmaceut Sci Key Lab Trop Biol Resources Minist Educ Haikou 570228 Hainan Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China

Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle closure (cc) has proven to be an effective method in improving the calculation accuracy by correcting the hysteresis (summation of errors) of each closed cycle to the theoretical value 0. However, a primary hypothesis was made in the current cycle closure method that the hysteresis is evenly distributed to all paths, which is unlikely to be true in practice and may limit the further improvement of the calculation accuracy when better error estimation methods are available. Moreover, being a closed source software makes the current cycle closure method unachievable in many studies. In this paper, a newly implemented open source graph-based weighted cycle closure (wcc) algorithm was developed and introduced, not only including functions from the original cc method but also containing a new wcc method which can consider different error contributions from different paths and further improve the calculation accuracy. The wcc program also provides a new path-independent molecular error calculation method, which can be quite useful in many studies (like structure-activity relationship (SAR)) compared with the path-dependent method of the original cc program.

关键词： Chemical calculations Free energy Genetics Ligands Molecules

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A Scalable parallel Algorithm for 3-D Magnetotelluric Finite Element Modeling in Anisotropic Media

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Zhu, Xiaoxiong Liu, Jie Cui, Yian Gong, Chunye Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Lab Software Engn Complex Syst Changsha 410073 Peoples R China Cent South Univ Sch Geosci & Info Phys Changsha 410083 Peoples R China

3-D magnetotelluric (MT) forward modeling has always been faced with the problems of high memory requirements and long computing time. In this article, we design a scalable parallel algorithm for 3-D MT finite element modeling in anisotropic media. The parallel algorithm is based on the distributed mesh storage, including multiple parallel granularities, and is implemented through multiple tools. Message-passing interface (MPI) is used to exploit process parallelisms for subdomains, frequencies, and solving equations. Thread parallelisms for merge sorting, element analysis, matrix assembly, and imposing Dirichlet boundary conditions are developed by Open Multi-Processing (OpenMP). We validate the algorithm through several model simulations and study the effects of topography and conductivity anisotropy on apparent resistivities and phase responses. Scalability tests are performed on the Tianhe-2 supercomputer to analyze the parallel performance of different parallel granularities. Three parallel direct solvers Supernodal LU (SUPERLU), MUltifrontal Massively parallel sparse direct Solver (MUMPS), and parallel Sparse matriX package (PASTIX) are compared in solving sparse systems of equations. As a result, reasonable parallel parameters are suggested for practical applications. The developed parallel algorithm is proven to be efficient and scalable.

关键词： Finite element analysis Mathematical model parallel algorithms Memory management Sparse matrices Conductivity Computational modeling Conductivity anisotropy finite element method (FEM) magnetotelluric (MT) parallel algorithm

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An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models

arXiv

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arXiv 2024年

作者： Liu, Qi Guo, Gang Mao, Jiaxin Dou, Zhicheng Wen, Ji-Rong Jiang, Hao Zhang, Xinyu Cao, Zhao Being Key Laboratory of Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China No.59 Zhongguancun Street Beijing100872 China Distributed and Parallel Software Lab Huawei Zhejiang No. 360 Jiangshu Road Hangzhou310056 China

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT and COIL) achieve state-of-the-art retrieval effectiveness by using all token embeddings to represent documents and queries and modeling their relevance with a sum-of-max operation. However, these fine-grained representations may cause unacceptable storage overhead for practical search systems. In this study, we systematically analyze the matching mechanism of these late-interaction models and show that the sum-of-max operation heavily relies on the co-occurrence signals and some important words in the document. Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models. We also leverage query pruning methods to further reduce the retrieval latency. We conduct extensive experiments on both in-domain and out-domain datasets and show that some of the used pruning methods can significantly improve the efficiency of these late-interaction models without substantially hurting their retrieval effectiveness. © 2024, CC BY.

关键词： Efficiency

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EPP-MVSNet: Epipolar-assembling based Depth Prediction for Multi-view Stereo

EPP-MVSNet: Epipolar-assembling based Depth Prediction for M...

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International Conference on Computer Vision (ICCV)

作者： Xinjun Ma Yue Gong Qirui Wang Jingwei Huang Lei Chen Fan Yu Distributed and Parallel Software Lab Huawei Technologies Hong Kong University of Science and Technology

ISBN: (纸本)9781665428132

In this paper, we proposed EPP-MVSNet, a novel deep learning network for 3D reconstruction from multi-view stereo (MVS). EPP-MVSNet can accurately aggregate features at high resolution to a limited cost volume with an optimal depth range, thus, leads to effective and efficient 3D construction. Distinct from existing works which measure feature cost at discrete positions which affects the 3D reconstruction accuracy, EPP-MVSNet introduces an epipolar-assembling-based kernel that operates on adaptive intervals along epipolar lines for making full use of the image resolution. Further, we introduce an entropy-based refining strategy where the cost volume describes the space geometry with the little redundancy. Moreover, we design a light-weighted network with Pseudo-3D convolutions integrated to achieve high accuracy and efficiency. We have conducted extensive experiments on challenging datasets Tanks & Temples(TNT), ETH3D and DTU. As a result, we achieve promising results on all datasets and the highest F-Score on the online TNT intermediate benchmark. Code is available at https://***/mindspore/mindspore/tree/master/model_zoo/research/cv/eppmvsnet.

关键词： Learning systems Costs Three-dimensional displays Image resolution Refining Redundancy Memory management

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Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons

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BMC BIOINFORMATICS 2022年第1期23卷 503-503页

作者： Mao, Guo Zeng, Ruigeng Peng, Jintao Zuo, Ke Pang, Zhengbin Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Deya Rd Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Deya Rd Changsha 410073 Peoples R China

Background Building biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regulatory relationships are complex and often nonlinear or non-monotonic, which makes inference using linear models challenging. Results In this paper, we propose a multi-layer perceptron-based differential equation method, which operates by training a fully connected neural network (NN) to simulate the transcription rate of genes in traditional differential equations. We verify whether the regulatory network constructed by the NN method can continue to achieve the expected biological function by verifying the degree of overlap between the regulatory network discovered by NN and the regulatory network constructed by the Hill function. And we validate our approach by adapting to noise signals, regulator knockout, and constructing large-scale gene regulatory networks using link-knockout techniques. We apply a real dataset (the mesoderm inducer Xenopus Brachyury expression) to construct the core topology of the gene regulatory network and find that Xbra is only strongly expressed at moderate levels of activin signaling. Conclusion We have demonstrated from the results that this method has the ability to identify the underlying network topology and functional mechanisms, and can also be applied to larger and more complex gene network topologies.

关键词： Fully connected neural network Biological function Differential equations Dynamical systems Link knockout

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