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Trilinos: Enabling Scientific Computing Across Diverse Hardware Architectures at Scale

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arXiv 2025年

作者： Mayr, Matthias Heinlein, Alexander Glusa, Christian Rajamanickam, Sivasankaran Arnst, Maarten Bartlett, Roscoe A. Berger-Vergiat, Luc Boman, Erik G. Devine, Karen Harper, Graham Heroux, Michael Hoemmen, Mark Hu, Jonathan Kelley, Brian Kim, Kyungjoo Kouri, Drew P. Kuberry, Paul Liegeois, Kim Ober, Curtis C. Pawlowski, Roger Pearson, Carl Perego, Mauro Phipps, Eric Ridzal, Denis Roberts, Nathan V. Siefert, Christopher M. Thornquist, Heidi K. Tomasetti, Romin Trott, Christian R. Tuminaro, Raymond S. Willenbring, James M. Wolf, Michael M. Yamazaki, Ichitaro Data Science & Computing Lab Institute for Mathematics and Computer-Based Simulation Universität der Bundeswehr München Werner-Heisenberg-Weg 39 Neubiberg85577 Germany Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Sandia National Laboratories 1515 Eubank Blvd SE AlbuquerqueNM87123 United States University of Liege Quartier Polytech 1 allée de la Découverte 9 Liege4000 Belgium ParaTools Inc. 18125 Kreigle Lake Road AvonMN56310 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Advanced Micro Devices Inc. 2485 Augustine Dr Santa ClaraCA95054 United States

Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to "An Overview of the Trilinos project" by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397–423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This paper also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and *** Codes 65-04, 65Y05 Copyright © 2025, The Authors. All rights reserved.

关键词： Open source software

Pruning randomly initialized neural networks with iterative randomization 21

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Pruning randomly initialized neural networks with iterative ...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Daiki Chijiwa Shin'ya Yamaguchi Yasutoshi Ida Kenji Umakoshi Tomohiro Inoue NTT Computer and Data Science Laboratories NTT Corporation NTT Social Informatics Laboratories NTT Corporation

ISBN: (纸本)9781713845393

Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis. Ramanujan et al. [23] empirically showed that only pruning the weights can achieve remarkable performance instead of optimizing the weight values. However, to achieve the same level of performance as the weight optimization, the pruning approach requires more parameters in the networks before pruning and thus more memory space. To overcome this parameter inefficiency, we introduce a novel framework to prune randomly initialized neural networks with iteratively randomizing weight values (IteRand). Theoretically, we prove an approximation theorem in our framework, which indicates that the randomizing operations are provably effective to reduce the required number of the parameters. We also empirically demonstrate the parameter efficiency in multiple experiments on CIFAR-10 and ImageNet.

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Efficient Algorithm for K-Multiple-Means

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Proceedings of the ACM on Management of data 2024年第1期2卷 1-26页

作者： Yasuhiro Fujiwara Atsutoshi Kumagai Yasutoshi Ida Masahiro Nakano Makoto Nakatsuji Akisato Kimura NTT Communication Science Laboratories Atsugi-shi Kanagawa Japan NTT Computer and Data Science Laboratories Musashino-shi Tokyo Japan NTT Human Informatics Laboratories Yokosuka-shi Kanagawa Japan

K-Multiple-Means is an extension of K-means for the clustering of multiple means used in many applications, such as image segmentation, load balancing, and blind-source separation. Since K-means uses only one mean to represent each cluster, it fails to capture non-spherical cluster structures of data points. However, since K-Multiple-Means represents the cluster by computing multiple means and grouping them into specified c clusters, it can effectively capture the non-spherical clusters of the data points. To obtain the clusters, K-Multiple-Means updates a similarity matrix of a bipartite graph between the data points and the multiple means by iteratively computing the leading c singular vectors of the matrix. K-Multiple-Means, however, incurs a high computation cost for large-scale data due to the iterative SVD computations. Our proposal, F-KMM, increases the efficiency of K-Multiple-Means by computing the singular vectors from a smaller similarity matrix between the multiple means obtained from the similarity matrix of the bipartite graph. To compute the similarity matrix of the bipartite graph efficiently, we skip unnecessary distance computations and estimate lower bounding distances between the data points and the multiple means. Theoretically, the proposed approach guarantees the same clustering results as K-Multiple-Means since it can exactly compute the singular vectors from the similarity matrix between the multiple means. Experiments show that our approach is several orders of magnitude faster than previous clustering approaches that use multiple means.

关键词： algorithm clustering efficient

Learning maximal ancestral graphs with robustness for faithfulness violations 2nd

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Learning maximal ancestral graphs with robustness for faithf...

2nd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015

作者： Isozaki, Takashi Kuroki, Manabu Sony Computer Science Laboratories Inc 3-14-13 Higashigotanda Shinagawa Tokyo141-0022 Japan Department of Data Science The Institute of Statistical Mathematics 10-3 Midori-cho Tachikawa Tokyo190-8562 Japan

ISBN: (纸本)9783319283784

Discovering causal models hidden in the background of observational data has been a difficult issue. It is often necessary to deal with latent common causes and selection bias for constructing causal models in real data. Ancestral graph models are effective and useful for representing causal models with latent variables. The causal faithfulness condition, which is usually assumed for determining the models, is statistically known to often be weakly violated for finite data. One of the authors developed a constraint-based causal learning algorithm that is robust against the violations while assuming no latent variables. In this study, we applied and extended the thoughts of the algorithm to the inference of ancestral graphs. The practical validity and effectiveness of the algorithm are also confirmed by using some standard datasets in comparison with FCI and RFCI algorithms. © Springer International Publishing Switzerland 2015.

关键词： Learning algorithms

Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures

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arXiv 2018年

作者： Nielsen, Frank Sun, Ke Sony Computer Science Laboratories Inc. Japan Data61 Australia

The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in [0;1]. This distance plays a fundamental role in machine learning and signal processing: It is a member of the broader class of f-divergences, and it is related to the probability of error in Bayesian hypothesis testing. Since the total variation distance does not admit closed-form expressions for statistical mixtures (like Gaussian mixture models), one often has to rely in practice on costly numerical integrations or on fast Monte Carlo approximations that however do not guarantee deterministic lower and upper bounds. In this work, we consider two methods for bounding the total variation of univariate mixture models: The first method is based on the information monotonicity property of the total variation to design guaranteed nested deterministic lower bounds. The second method relies on computing the geometric lower and upper envelopes of weighted mixture components to derive deterministic bounds based on density ratio. We demonstrate the tightness of our bounds in a series of experiments on Gaussian, Gamma and Rayleigh mixture models. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal processing

Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks

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arXiv 2022年

作者： Chijiwa, Daiki Yamaguchi, Shin'ya Kumagai, Atsutoshi Ida, Yasutoshi NTT Computer and Data Science Laboratories NTT Corporation Japan Kyoto University Japan

Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized NNs can easily overfit to such small dataset. Previous work (e.g. MAML by Finn et al. 2017) tackles this challenge by meta-learning, which learns how to learn from a few data by using various tasks. On the other hand, one conventional approach to avoid overfitting is restricting hypothesis spaces by endowing sparse NN structures like convolution layers in computer vision. However, although such manually-designed sparse structures are sample-efficient for sufficiently large datasets, they are still insufficient for few-shot learning. Then the following questions naturally arise: (1) Can we find sparse structures effective for few-shot learning by meta-learning? (2) What benefits will it bring in terms of meta-generalization? In this work, we propose a novel meta-learning approach, called Meta-ticket, to find optimal sparse subnetworks for few-shot learning within randomly initialized NNs. We empirically validated that Meta-ticket successfully discover sparse subnetworks that can learn specialized features for each given task. Due to this task-wise adaptation ability, Meta-ticket achieves superior meta-generalization compared to MAML-based methods especially with large NNs. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

q-Neurons: Neuron activations based on stochastic Jackson's derivative operators

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arXiv 2018年

作者： Nielsen, Frank Sun, Ke Sony Computer Science Laboratories Inc. Japan Data61 Australia

We propose a new generic type of stochastic neurons, called q-neurons, that considers activation functions based on Jackson's q-derivatives, with stochastic parameters q. Our generalization of neural network architectures with q-neurons is shown to be both scalable and very easy to implement. We demonstrate experimentally consistently improved performances over state-of-the-art standard activation functions, both on training and testing loss functions. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

Effects of local minima and bifurcation delay on combinatorial optimization with continuous variables

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arXiv 2022年

作者： Sato, Shintaro NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan

Combinatorial optimization problems can be mapped onto Ising models, and their ground state is generally difficult to find. A lot of heuristics for these problems have been proposed, and one promising approach is to use continuous variables. In recent years, one such algorithm has been implemented by using parametric oscillators known as coherent Ising machines. Although these algorithms have been confirmed to have high performance through many experiments, unlike other familiar algorithms such as simulated annealing, their computational ability has not been fully investigated. In this paper, we propose a simple heuristic based on continuous variables whose static and dynamical properties are easy to investigate. Through the analyses of the proposed algorithm, we find that many local minima in the early stage of the optimization and bifurcation delay reduce its performance in a certain class of Ising models. Copyright © 2022, The Authors. All rights reserved.

关键词： Combinatorial optimization

Modeling, Capacity Studies, Antenna and System Designs for 6G/B6G 3D Continuous-Space Radio Channels Enabled by Electromagnetic Information Theory

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IEEE Communications Surveys and Tutorials 2025年

作者： Wang, Cheng-Xiang Li, Junling Huang, Jie Huang, Chen Zhang, Zhaoyang Liu, Ying Zhou, Shidong Chen, Yunfei You, Xiaohu Gao, Xiqi Cui, Tie Jun Renzo, Marco Di Thompson, John Haas, Harald Schober, Robert Zhang, Rui Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Science and Electronic Engineering Hangzhou310007 China Xidian University National Key Laboratory of Radar Detection and Sensing Xi'an710071 China Tsinghua University State Key Laboratory on Microwave and Digital Communications Beijing100084 China University of Durham Department of Engineering DurhamDH1 3LE United Kingdom Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing211189 China UniversitèParis-Saclay Laboratoire des Signaux et Systèmes CNRS CentraleSupélec Gif-sur-Yvette91192 France Centre for Telecommunications Research - Department of Engineering King's College London LondonWC2R 2LS United Kingdom School of Engineering EdinburghEH9 3BF United Kingdom Cambridge University Department of Engineering CambridgeCB2 1PZ United Kingdom Institute for Digital Communications Erlangen91054 Germany The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

Channel theory is a fundamental theory of wireless communications. The sixth generation (6G) and beyond 6G (B6G) wireless communication networks are expected to provide space-air-ground-sea global coverage. Base stations and users tend to move in three dimensional (3D) continuous spaces, while antennas and propagation environments can be tightly coupled. The underlying channels show an evolutionary trend to 3D continuous-space radio channels that combine antennas and wireless propagation channels, in comparison to discrete local-space wireless propagation channels in previous generations. This introduces new challenges for channel modeling, channel capacity analysis, antenna design, system design, etc. To address these challenges, this paper performs a comprehensive study on 3D continuous-space radio channels in 6G/B6G with the aid of electromagnetic information theory (EIT) that integrates electromagnetic theory, information theory, wireless propagation channel modeling theory, and antenna theory. We start by revealing the connections and gaps between these four fundamental theories. Then, an in-depth investigation on the four major research thrusts of 3D continuous-space radio channels is provided: 1) channel measurements and modeling, 2) channel capacity analysis, 3) general antenna design, and 4) wireless system design. We aim to explore the intrinsic relationships between antenna parameters, channel parameters, channel characteristics, channel capacity, and communication system performance. Finally, future research directions and challenges for 3D continuous-space radio channels are outlined. Our study endeavors to establish a fundamental framework for 3D continuous-space radio channels, with the potential to catalyze breakthroughs in 6G/B6G theories. © 1998-2012 IEEE.

关键词： Antenna grounds