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scalable User Rate and Energy-Efficiency Optimization in Cell-Free Massive MIMO

IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第9期70卷 6050-6065页

作者： Tuan, H. D. Nasir, A. A. Ngo, H. Q. Dutkiewicz, E. Poor, H., V Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia King Fahd Univ Petr & Minerals KFUPM Dept Elect Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals KFUPM Ctr Commun Syst & Sensing Dhahran 31261 Saudi Arabia Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT3 9DT Antrim North Ireland Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users' rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.

关键词： Manganese Channel estimation Optimization Resource management Random variables Quality of service Indexes Cell-free massive MIMO (cfm-MIMO) conjugate beamforming (CB) energy efficiency geometric mean nonconvex optimization scalable algorithms

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HUGE: Huge Unsupervised Graph Embeddings with TPUs 23

HUGE: Huge Unsupervised Graph Embeddings with TPUs

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29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Mayer, Brandon A. Tsitsulin, Anton Fichtenberger, Hendrik Halcrow, Jonathan Perozzi, Bryan Google Res Mountain View CA 94043 USA Google Res Zurich Switzerland

ISBN: (纸本)9798400701030

Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial and academic need to quickly analyze graphs with billions of nodes and trillions of edges. A common first step for network understanding is Graph Embedding, the process of creating a continuous representation of nodes in a graph. A continuous representation is often more amenable, especially at scale, for solving downstream machine learning tasks such as classification, link prediction, and clustering. A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges. We verify the embedding space quality on real and synthetic large-scale datasets.

关键词： graph embedding scalable algorithms tensor processing units

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Robust and scalable Methods for the Dynamic Mode Decomposition*

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SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS 2022年第1期21卷 60-79页

作者： Askham, Travis Zheng, Peng Aravkin, Aleksandr Kutz, J. Nathan New Jersey Inst Techonol Dept Appl Math Newark NJ 07102 USA Univ Washington Dept Hlth Metr Sci Seattle WA 98195 USA Univ Washington Dept Appl Math Seattle WA 98195 USA

The dynamic mode decomposition (DMD) is a broadly applicable dimensionality reduction algorithm that decomposes a matrix of time-series data into a product of a matrix of exponentials, representing Fourier-like time dynamics, and a matrix of coefficients, representing spatial structures. This interpretable spatio-temp oral decomposition is classically formulated as a nonlinear least squares problem and solved within the variable projection framework. When the data contains outliers, or other features that are not well represented by exponentials in time, the standard Frobenius norm misfit penalty creates significant biases in the recovered time dynamics. As a result, practitioners are left to clean such defects from the data manually or to use a black-box cleaning approach like robust principal component analysis (PCA). As an alternative, we propose a robust statistical framework for the optimization used to compute the DMD itself. We also develop variable projection algorithms for these new formulations, which allow for regularizers and constraints on the decomposition parameters. Finally, we develop a scalable version of the algorithm by combining the structure of the variable projection framework with the stochastic variance reduction (SVRG) paradigm. The approach is tested on a range of synthetic examples, and the methods are implemented in an open source software package RobustDMD.

关键词： robust statistics dynamic mode decomposition scalable algorithms

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CoSimHeat: An Effective Heat Kernel Similarity Measure Based on Billion-Scale Network Topology 22

CoSimHeat: An Effective Heat Kernel Similarity Measure Based...

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31st ACM Web Conference (WWW)

作者： Yu, Weiren Jian Yang Zhang, Maoyin Di Wu Univ Warwick Coventry England Nanjing Univ Sci & Technol Jiangsu Peoples R China Nanjing Univ Sci & Technol Jiangsu Jiangsu Peoples R China Hunan Univ Changsha Peoples R China

ISBN: (纸本)9781450390965

Myriads of web applications in the Big Data era demand an effective measure of similarity based on billion-scale network structures, e.g., collaborative filtering. Recently, CoSimRank has been devised as a promising graph-theoretic similarity model, which iteratively captures the notion that "two distinct nodes are evaluated as similar if they are connected with similar nodes". However, the existing CoSimRank model for assessing similarities may either yield unsatisfactory results or rather cost-inhibitive, rendering it impractical in massive graphs. In this paper, we propose CoSimHeat, a novel scalable graph-theoretic similarity model based on heat diffusion. Specifically, we first formulate CoSimHeat model by taking advantage of heat diffusion to emulate the activities of similarity propagations on the Web. Then, we show that the similarities produced by CoSimHeat are more satisfactory than those from CoSimRank families since CoSimHeat fulfils four axioms that an ideal similarity model should satisfy while circumventing the "dead-loop" problem of CoSimRank. Next, we propose a fast algorithm to substantially accelerate CoSimHeat computations on billion-sized graphs, with guarantees of accuracy. Our experiments on various datasets validate that CoSimHeat achieves higher accuracy and is order-of-magnitude faster than state-of-the-art competitors.

关键词： Similarity Search Link Analysis scalable algorithms

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HPC enabled a Novel Deep Fuzzy scalable Clustering Algorithm and its Application for Protein Data

HPC enabled a Novel Deep Fuzzy Scalable Clustering Algorithm...

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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)

作者： Jha, Preeti Tiwari, Aruna Bharill, Neha Ratnaparkhe, Milind Patel, Om Prakash Anand, Vaibhav Arya, Sudhanshu Singh, Tanmay Indian Inst Technol Indore Comp Sci & Engn Indore India Mahindra Univ Comp Sci & Engn Hyderabad India ICAR Indian Inst Soybean Res Biotechnol Indore India

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

Fuzzy clustering is a common way to divide data into groups. Even though it has been improved a lot, fuzzy clustering still has problems while clustering real high-dimensional Big Data with complicated latent distributions. To solve this problem, this study comes up with a way to represent the data in a feature space that was built from a scalable deep neural network using Apache Spark on HPC. In this paper, we proposed SDnnRSIO-FCM, a scalable Deep Neural Network Random Sampling Iterative Optimization-FCM clustering algorithm, and the SDnnLFCM, a scalable version of the Deep Neural Network Literal Fuzzy c-Means algorithm. We focus on the design and implementation of the proposed SDnnRSIO-FCM and SDnnLFCM algorithms using the Apache Spark cluster in a High-Performance Computing (HPC) environment by representing the data in a feature space produced by the neural network to handle Big Data. First, data is mapped into new feature space to aid in the reconstruction of the original data by providing a good representation. Second, scalable fuzzy clustering is embedded with neural networks to propose deep fuzzy clustering methods. The experimental results conducted on two huge benchmark datasets show that the SDnnRSIO-FCM algorithm outperforms the SDnnLFCM algorithm in terms of Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and F-score. Furthermore, the proposed SDnnRSIO-FCM applied to huge soybean protein sequences in comparison with SDnnLFCM shows a significant improvement in terms of Silhouette index (SI), Davies-Bouldin index (DBI), and Calinski-Harabasz index (CHI).

关键词： Fuzzy Clustering Deep Learning Big Data Neural Network scalable algorithms

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An Empirical Investigation of OpenMP Based Implementation of Simplex Algorithm 17th

An Empirical Investigation of OpenMP Based Implementation of...

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17th International Workshop on OpenMP (IWOMP)

作者： Banerjee, Arkaprabha Shah, Pratvi Nandani, Shivani Tyagi, Shantanu Kumar, Sidharth Chaudhury, Bhaskar DA IICT Grp Computat Sci & High Performance Comp Gandhinagar India Univ Alabama Birmingham Birmingham AL USA

ISBN: (纸本)9783030852627;9783030852610

This paper presents a shared-memory based parallel implementation of the standard simplex algorithm. The simplex algorithm is a popular technique for linear programming used to solve minimization and maximization problems that are subject to linear constraints. The simplex algorithm reduces the optimization problem to a series of iterative matrix operations. In this paper we perform an empirical analysis of our algorithm and also study the impact of the density of the underlying matrix on the overall performance. We observed a maximum speedup of 10.2 at 16 threads and also demonstrated that our proposed parallel algorithm scales well over a range of matrix densities. We also make an important observation that the effect of increasing the number of constraints is more significant than the effect of varying the number of variables.

关键词： Large-scale problems Linear programming Simplex Parallel computing scalable algorithms OpenMP

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Large-scale periodic scheduling in time-sensitive networks

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COMPUTERS & OPERATIONS RESEARCH 2022年 137卷 105512-105512页

作者： Vlk, Marek Brejchova, Katerina Hanzalek, Zdenek Tang, Siyu Czech Tech Univ Czech Inst Informat Robot & Cybernet Jugoslavskych Partyzanu 1580-3 Prague 16000 6 Czech Republic Charles Univ Prague Fac Math & Phys Malostranske Namesti 25 Prague 11800 1 Czech Republic Huawei Technol Duesseldorf GmbH Appl Network Technol Lab Munich Res Ctr Riesstr 253-0G D-80992 Munich Germany

The real-time and safety-critical demands posed in a variety of industrial systems can now be handled on the Ethernet architecture by virtue of the IEEE Time-Sensitive Networking task group. In particular, deterministic nature and timeliness guarantees can be achieved by time-triggered communication schedules that are enabled by the IEEE 802.1Qbv standard. However, generating such a schedule is a computationally challenging task due to several harsh constraints that must be satisfied. This problem is usually transformed into some existing formalism, e.g., Satisfiability Modulo Theories, and solved using a general third-party solver, which has the drawback of limited scalability. Except for some one-pass heuristics, such as the earliest deadline first or as-soon-as-possible scheduling, the current literature does not provide any algorithm tailored to this specific problem of scheduling time-triggered traffic in time-sensitive networks. In this paper, we develop an efficient algorithm that does not use any third-party solver and is based on the search in the space of partial schedules while being guided by the discovered conflicts. Our algorithm calculates schedules for problem instances consisting of 2000 network nodes and more than 10 000 flows.

关键词： Time-sensitive networking Periodic scheduling Time-triggered message scheduling Periodic job-shop extensions scalable algorithms Heuristics

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Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2022年第4期40卷 78-78页

作者： Yu, Weiren McCann, Julie Zhang, Chengyuan Ferhatosmanoglu, Hakan Univ Warwick Dept Comp Sci Coventry CV4 7EZ W Midlands England Imperial Coll Dept Comp London SW7 2RH England Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

SimRank is an attractive link-based similarity measure used in fertile fields of Web search and sociometry. However, the existing deterministic method by Kusumoto et al. [24] for retrieving SimRank does not always produce high-quality similarity results, as it fails to accurately obtain diagonal correction matrix D. Moreover, SimRank has a "connectivity trait" problem: increasing the number of paths between a pair of nodes would decrease its similarity score. The best-known remedy, SimRank+ + [I], cannot completely fix this problem, since its score would still be zero if there are no common in-neighbors between two nodes. In this article, we study fast high-quality link-based similarity search on billion-scale graphs. (1) We first devise a "varied-D" method to accurately compute SimRank in linear memory. We also aggregate duplicate computations, which reduces the time of [24] from quadratic to linear in the number of iterations. (2) We propose a novel "cosine-based" SimRank model to circumvent the "connectivity trait" problem. (3) To substantially speed up the partial-pairs "cosine-based" SimRank search on large graphs, we devise an efficient dimensionality reduction algorithm, PSR#, with guaranteed accuracy. (4) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument in [24] that "if D is replaced by a scaled identity matrix (1 - gamma), their top-K rankings will not be affected much". (5) We propose a novel method that can accurately convert from Li et al. SimRank S to Jeh and Widom's SimRank S. (6) We propose GSR 5 , a generalisation of our "cosine-based" SimRank model, to quantify pairwise similarities across two distinct graphs, unlike SimRank that would assess nodes across two graphs as completely dissimilar. Extensive experiments on various datasets demonstrate the superiority of our proposed approaches in terms of high search quality, computational efficiency, accuracy, and scalability on billion-edge

关键词： Similarity search link analysis scalable algorithms

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Efficient and dimension independent methods for neural network surrogate construction and training

Efficient and dimension independent methods for neural netwo...

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学位级别：博士

In this dissertation I investigate how to efficiently construct neural network surrogates for parametric maps defined by PDEs, and how to use second order information to improve solutions to the related neural network training problem. Many-query problems arising in scientific applications (such as optimization, uncertainty quantification and inference problems) require evaluation of an input output mapping parametrized by a high dimensional nonlinear PDE model. The cost of these evaluations makes solution using the model prohibitive, and efficient accurate surrogates are the key to solving these problems in practice. In this work I investigate neural network surrogates that use model information to detect informed subspaces of the input and output where the parametric map can be represented efficiently. These compact representations require relatively few data to train and outperform conventional data-driven approaches which require large training data sets. Once a neural network is designed, training is a major issue. One seeks to find optimal weights for a neural network that generalize to data not seen during training. In this work I investigate how second order information can be efficiently exploited to design optimizers that have fast convergence and good generalization properties. These optimizers are shown to outperform conventional methods in numerical experiments.

关键词： Optimization Machine learning Surrogate modeling Reduced order methods Matrix-free methods Neural networks Uncertainty quantification scalable algorithms Randomized methods

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scalable EXTENDED DYNAMIC MODE DECOMPOSITION USING RANDOM KERNEL APPROXIMATION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2019年第3期41卷 A1482-A1499页

作者： Degennaro, Anthony M. Urban, Nathan M. Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA Los Alamos Natl Lab Comp Computat & Stat Sci Los Alamos NM 87545 USA

The Koopman operator is a linear, infinite-dimensional operator that governs the dynamics of system observables;extended dynamic mode decomposition (EDMD) is a data-driven method for approximating the Koopman operator using functions (features) of the system state snapshots. This paper investigates an approach to EDMD in which the features used provide random approximations to a particular kernel function. The objective of this is computational economy for large data sets: EDMD is generally ill-suited for problems with large state dimension, and its dual kernel formulation (KDMD) is well-suited for such problems only if the number of data snapshots is relatively small. We discuss two specific methods for generating features: random Fourier features and the Nystrom method. The first method is a data-independent method for translation-invariant kernels only and involves random sampling in feature space;the second method is a data-dependent empirical method that may be used for any kernel and involves random sampling of data. We first discuss how these ideas may be applied in an EDMD context, as well as a means for adaptively adding random Fourier features. We demonstrate these methods on two example problems and conclude with an analysis of the relative benefits and drawbacks of each method.

关键词： dynamic mode decomposition system identification scalable algorithms dynamical systems low-dimensional modeling model inference

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