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检索条件"机构=Laboratory of Parallel Software and Computational Science of Software"

共 591 条记录，以下是41-50 订阅

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Performance Portability of Electron Repulsion Integrals and Their Related Methods across Peta to Exascale Architectures 24

Performance Portability of Electron Repulsion Integrals and ...

引用

Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Tosaporn Sattasathuchana Peng Xu Dossay Oryspayev Colleen Bertoni Luke B. Roskop Mark S. Gordon Department of Chemistry University of Hawai'i at Manoa Honoluly HI United States Ames National Laboratory Department of Chemistry Iowa State University Ames IA United States Institute for Advanced Computational Science Stony Brook University Stony Brook NY United States Leadership Computing Facility Argonne National Laboratory Lemont IL United States HPC Systems Software Hewlett Packard Enterprise Bloomington MN United States

ISBN: (纸本)9798350355543

Entering the exascale era, graphical processing units (GPUs) have been developed by various vendors for large-scale HPC machines. Nine of the top 10 computers on the Top500 List have heterogeneous architectures containing GPUs as accelerators. To take advantage of these computational resources, it is essential to maintain a balance between performance and portability, i.e., making the codes functional, performant, and portable across GPU vendors. In this work, the efforts of restructuring and porting several key CPU kernels in the GAMESS quantum chemistry software package to AMD, Intel, and NVIDIA GPUs via an OpenMP API will be discussed. The use of the OpenMP directive model allows one to successfully run the same code across many GPUs of different vendors. However, since OpenMP is a high-level programming model, it relies on vendor implementations to handle the GPU code generation, and since implementations vary, there are large differences in performance even on the same hardware with different vendor compilers. Presented here are challenges faced during porting CPU code to GPU code with OpenMP, which other porting efforts may also face, such as memory limitations, major restructuring needed for code with branching, and differences in compiler optimizations and tuning. The strategies and approaches to address these challenges are discussed. In this work, the performance results across the supercomputing systems, Summit, Aurora, Frontier, and Perlmutter with a variety of vendor software stacks are presented.

关键词： Aurora

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software Component Library Management Mechanism for Equipment Parts Service Value-Net 6th

Software Component Library Management Mechanism for Equipmen...

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6th International Conference on Human Centered Computing, HCC 2020

作者： Tian, Zhuo Zhang, Changyou Cai, Xiaofeng Xiao, Jiaojiao Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9783030706258

software reuse realizes the sharing of software resources, and component-based reuse is the main form of software reuse. The classification, storage, retrieval, and release of a large number of component resources require efficient component library management methods. This paper proposes an open software component library management mechanism based on microservices. Taking manufacturing product service life cycle value chain collaboration as a case, it realizes a domain-oriented microservice granularity division mechanism and forms a domain-oriented software component representation, warehousing, discovery, selection, arrangement, filing, and other compatible open software component library management mechanisms;realization of the design of an open software component management system for manufacturing product operation and maintenance reengineering value chain collaboration, The simulation verifies the open ability and management efficiency of the management mechanism. © 2021, Springer Nature Switzerland AG.

关键词： Computer software reusability

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SSSP on GPU without atomic operation 2nd

SSSP on GPU without atomic operation

引用

2nd International Conference on Human Centered Computing, HCC 2016

作者： Wang, Feng Zhu, Liehuang Zhang, Changyou School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9783319318530

Graph is a general theoretical model in many large scale data-driven applications. SSSP (Single Source Shortest Path) algorithm is a foundation for most important algorithms and applications. GPU remains its mainstream station in high performance computing with heterogeneous architecture computers. Because of the high parallelization of the GPU threads, the distances of the vertices of the GPU are updated by atomic operations to avoid the read and write errors. Most atomic operations are unnecessary since the read-write conflicts are rare in large graph. However, without atomic operations the result accuracy can’t be guaranteed. The atomic operations take large part of the running time of the program. To improve the performance of SSSP on GPU, we proposed an algorithm with data block iterations instead of atomic operations. The algorithm not only gets a high speed-up but also guarantees the accuracy of the result. Experimental results show that this SSSP algorithm gained a speedup of three times than the serial algorithm on CPU and more than ten times than the parallel algorithm on GPU with atomic operation. © Springer International Publishing Switzerland 2016.

关键词： Graphics processing unit

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An Efficient Algorithm for Solving Eigenproblem

An Efficient Algorithm for Solving Eigenproblem

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The 13th International Symposium on Distributed Computing and Applications to Business,Engineering and science(DCABES 2014)(第十三届分布式计算及其应用国际学术研讨会)

作者： Huirong Zhang Jianwen Cao Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese

ISBN: (纸本)9781479941681

In this paper,we consider second order elliptic ODE eigenproblems on general *** construct an efficient algorithm for computing the eigenvalue by using weighted mean combination of the linear finite element method and corresponding 2nd-order finite difference *** first take the arithmetic mean of the two *** we compute the quasi-optimal combined parameters for different eigenvalues to improve our efficient *** algorithm we construct convergence faster and have higher accuracy than the linear finite element method and corresponding 2nd-order finite difference *** numerical examples tested on both uniform meshes and nonuniform meshes are given to illustrate the computational cost of different numerical methods for solving eigenvalue *** efficiency,all the matrices use sparse storage in our algorithm.

关键词： Keywords-Efficient algorithm combinatorial quasioptimal eigenproblem

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The Research and Analysis of Hungarian Algorithm in the Structure Index Reduction for DAE

The Research and Analysis of Hungarian Algorithm in the Stru...

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The Eleventh International Symposium on Distributed Computing and Applications to Business,Engineering and science(DCABES2012)(第十一届分布式计算及其应用国际学术研讨会)(原名:电子商务、工程及科学领域的分布式计算和应用国际学术研讨会)

作者： Yan Zeng Xuesong Wu Jianwen Cao Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese

Modeling of complex physical systems with Modelica usually leads to the high-index differential algebraic equation system(DAE),index reduction is an important part of solving the high-index *** structure index reduction algorithm is one of the popular methods,but in special cases,it *** relaxation algorithm can detect and correct the breakdown *** the maximum weight matching of bipartite graph is an important part of the combinatorial relaxation *** order to choose the proper method for the large-scale,dense bipartite graph,this paper provides three implementations of the Hungarian *** experiment results and the theory show that the BFS single-augmented method is better than others.

关键词： Modelica Bipartite Graph DAE Hungarian Algorithm Augmenting Path

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PRELUDE - A SYSTEM FOR PORTABLE parallel software 4th

PRELUDE - A SYSTEM FOR PORTABLE PARALLEL SOFTWARE

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4TH INTERNATIONAL PARLE CONF ( parallel ARCHITECTURES AND LANGUAGES EUROPE )

作者： WEIHL, W BREWER, E COLBROOK, A DELLAROCAS, C HSIEH, W JOSEPH, A WALDSPURGER, C WANG, P Large-Scale Parallel Software Group MIT Laboratory for Computer Science United States

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parallel Construction of Approximate kNN Graph

Parallel Construction of Approximate kNN Graph

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作者： Dilin Wang Jianwen Cao Yanmei Zheng Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese

Building k-nearest neighbor(kNN)graphs is a necessary step in such areas as data mining and machine *** in this paper,we attempt to study the kNN furthermore,we first propose a parallel algorithm for approximate kNN graph construction and then apply the kNN graph to the application of *** show that our MPI/OpenMP mixed mode codes can make the construction of approximate kNN graph faster and make the parallelization and implementation ***,we compare the results of agglomerative clustering methods by using our parallel algorithm to illustrate the applicability of this method.

关键词： Approximate kNN MPI/OpenMP parallel algorithms Clustering

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PAD: A Robustness Enhancement Ensemble Method via Promoting Attention Diversity 30

PAD: A Robustness Enhancement Ensemble Method via Promoting ...

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Joint 30th International Conference on computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yang, Yuting Huang, Pei Ma, Feifei Cao, Juan Li, Jintao China University of Chinese Academy of Sciences China Department of Computer Science Stanford University United States State Key Laboratory of Computer Science Institute of Software CAS China Laboratory of Parallel Software and Computational Science Institute of Software CAS China

ISBN: (纸本)9782493814104

Deep neural networks can be vulnerable to adversarial attacks, even for the mainstream Transformer-based models. Although several robustness enhancement approaches have been proposed, they usually focus on some certain type of perturbation. As the types of attack can be various and unpredictable in practical scenarios, a general and strong defense method is urgently in require. We notice that most well-trained models can be weakly robust in the perturbation space, i.e., only a small ratio of adversarial examples exist. Inspired by the weak robust property, this paper presents a novel ensemble method for enhancing robustness. We propose a lightweight framework PAD to save computational resources in realizing an ensemble. Instead of training multiple models, a plugin module is designed to perturb the parameters of a base model which can achieve the effect of multiple models. Then, to diversify adversarial example distributions among different models, we promote each model to have different attention patterns via optimizing a diversity measure we defined. Experiments on various widely-used datasets and target models show that PAD can consistently improve the defense ability against many types of adversarial attacks while maintaining accuracy on clean data. Besides, PAD also presents good interpretability via visualizing diverse attention patterns. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Deep neural networks

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Learning the Satisfiability of Pseudo-Boolean Problem with Graph Neural Networks 26th

Learning the Satisfiability of Pseudo-Boolean Problem with G...

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26th International Conference on Principles and Practice of Constraint Programming, CP 2020

作者： Liu, Minghao Zhang, Fan Huang, Pei Niu, Shuzi Ma, Feifei Zhang, Jian State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030584740

Graph Neural Network (GNN) has shown great power on many practical tasks in the past few years. It is also considered to be a potential technique in bridging the gap between machine learning and symbolic reasoning. Experimental investigations have also shown that some-Hard constraint satisfaction problems can be well learned by the GNN models. In this paper, a GNN-based classification model to learn the satisfiability of pseudo-Boolean (PB) problem is proposed. After constructing the bipartite graph representation, a two-phase message passing process is executed. Experiments on 0–1 knapsack and weighted independent set problems show that the model can effectively learn the features related to the problem distribution and satisfiability. As a result, competitive prediction accuracy has been achieved with some generalization to larger-scale problems. The studies indicate that GNN has great potential in solving constraint satisfaction problems with numerical coefficients. © 2020, Springer Nature Switzerland AG.

关键词： Graph neural networks

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Can Graph Neural Networks Learn to Solve the MaxSAT Problem? 37

Can Graph Neural Networks Learn to Solve the MaxSAT Problem?

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Liu, Minghao Huang, Pei Jia, Fuqi Zhang, Fan Sun, Yuchen Cai, Shaowei Ma, Feifei Zhang, Jian State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Inspir.ai

ISBN: (纸本)9781577358800

The paper presents an attempt to bridge the gap between machine learning and symbolic reasoning. We build graph neural networks (GNNs) to predict the solution of the Maximum Satisfiability (MaxSAT) problem, an optimization variant of SAT. Two closely related graph representations are adopted, and we prove their theoretical equivalence. We also show that GNNs can achieve attractive performance to solve hard MaxSAT problems in certain distributions even compared with state-of-the-art solvers through experimental evaluation. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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