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Scalable Reactive Molecular Dynamics Simulations for Computational Synthesis

COMPUTING IN SCIENCE & ENGINEERING 2019年第5期21卷 64-75页

作者： Li, Ying Nomura, Ken-Ichi Insley, Joseph A. Morozov, Vitali Kumaran, Kalyan Romero, Nichols A. Goddard, William A., III Kalia, Rajiv K. Nakano, Aiichiro Vashishta, Priya Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Univ Southern Calif Chem Engn & Mat Sci Los Angeles CA USA CALTECH Chem Mat Sci & Appl Phys Pasadena CA 91125 USA Univ Southern Calif Phys & Astron Chem Engn & Mat Sci & Comp Sci Los Angeles CA USA Univ Southern Calif Comp Sci Phys & Astron Chem Engn & Mat Sci & Biol Los Angeles CA USA Univ Southern Calif Chem Engn & Mat Sci Phys & Astron & Comp Sci Los Angeles CA USA

Reactive molecular dynamics (MD) simulation is a powerful research tool for describing chemical reactions. We eliminate the speed-limiting charge iteration in MD with a novel extended-Lagrangian scheme. The extended-Lagrangian reactive MD (XRMD) code drastically improves energy conservation while substantially reducing time-to-solution. Furthermore, we introduce a new polarizable charge equilibration (PQEq) model to accurately predict atomic charges and polarization. The XRMD code based on hybrid message passing+multithreading achieves a weak-scaling parallel efficiency of 0.977 on 786 432 IBM Blue Gene/Q cores for a 67.6 billion-atom system. The performance is portable to the second-generation Intel Xeon Phi, Knights Landing. Blue Gene/Q simulations for the computational synthesis of materials via novel exfoliation mechanisms for synthesizing atomically thin transition metal dichalcogenide layers will dominate nanomaterials science in this century.

关键词： Charge Exchange Coulomb Blockade Decomposition Energy Conservation Iterative Methods Lagrangian Field Theory Message Passing Molecular Dynamics Method Multi Threading Potential Energy Functions Atomically Thin Transition Metal Dichalcogenide Layers Billion Atom System Weak Scaling parallel Efficiency XRMD Code Polarization Atomic Charges Polarizable Charge Equilibration Model Time To Solution Energy Conservation Extended Lagrangian Reactive MD Code Novel Extended Lagrangian Scheme Speed Limiting Charge Chemical Reactions Powerful Research Tool Reactive Molecular Dynamics Simulation Computational Synthesis Scalable Reactive Molecular Dynamics Simulations Computational Modeling Multithreading Mathematical Model Materials Science And Technology Numerical Models Computer Science Predictive Models Computing Methodologies Simulation Modeling And Visualization Simulation Theory Types Of Simulation Super Very Large Computers Large And Medium X 201 C Mainframe X 201 D Computers Computer System Implementation Mathematics Of Computing Numerical Analysis General parallel algorithms Software Software Engineering Operating Systems Performance Modeling And Prediction Theory Of Computation General Data General System Applications And Experience

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Estimation of Graphlet Counts in Massive Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019年第1期30卷 44-57页

作者： Rossi, Ryan A. Zhou, Rong Ahmed, Nesreen K. Adobe Res San Jose CA 95110 USA Google Mountain View CA 94043 USA Intel Labs Santa Clara CA 95052 USA

Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms;however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually sufficient for many applications. In this paper, we propose an unbiased graphlet estimation framework that is: (a) fast with large speedups compared to the state of the art;(b) parallel with nearly linear speedups;(c) accurate with less than 1% relative error;(d) scalable and space efficient for massive networks with billions of edges;and (e) effective for a variety of real-world settings as well as estimating global and local graphlet statistics (e. g., counts). On 300 networks from 20 domains, we obtain < 1% relative error for all graphlets. This is vastly more accurate than the existing methods while using less data. Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.

关键词： Graphlets network motifs induced subgraphs estimation methods unbiased graphlet estimation local graphlet count estimation graphlet statistics parallel algorithms higherorder network analysis machine learning

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Simple parallel biconnectivity algorithms for multicore platforms

Simple parallel biconnectivity algorithms for multicore plat...

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International Conference on High Performance Computing

作者： George M. Slota Kamesh Madduri Department of Computer Science and Engineering The Pennsylvania State University University Park PA USA

We present two new algorithms for finding the biconnected components of a large undirected sparse graph. The first algorithm is based on identifying articulation points and labeling edges using multiple connectivity queries, and the second approach uses the color propagation technique to decompose the graph. Both methods use a breadth-first spanning tree and some auxiliary information computed during Breadth-First Search (BFS). These methods are simpler than the Tarjan-Vishkin PRAM algorithm for biconnectivity and do not require Euler tour computation or any auxiliary graph construction. We identify steps in these algorithms that can be parallelized in a shared-memory environment and develop tuned OpenMP implementations. Using a collection of large-scale real-world graph instances, we show that these methods outperform the state-of-the-art Cong-Bader biconnected components implementation, which is based on the Tarjan-Vishkin algorithm. We achieve up to 7.1× and 4.2× parallel speedup over the serial Hopcroft-Tarjan and parallel Cong-Bader algorithms, respectively, on a 16-core Intel Sandy Bridge system. For some graph instances, due to the fast BFS-based preprocessing step, the single-threaded implementation of our first algorithm is faster than the serial Hopcroft-Tarjan algorithm.

关键词： Algorithm design and analysis Labeling Phase change random access memory parallel algorithms Bridges Program processors

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The DiamondCandy LRnLA algorithm: raising efficiency of the 3D cross-stencil schemes

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JOURNAL OF SUPERCOMPUTING 2019年第12期75卷 7778-7789页

作者： Perepelkina, Anastasia Levchenko, Vadim Khilkov, Sergey Keldysh Inst Appl Math Miusskaya Sq 4 Moscow 125047 Russia HIPERCONE Ltd 3rd Khoroshevskaya Str12 Moscow 123298 Russia

The parallel efficiency is raised by increasing the locality of calculation. With the locally recursive non-locally asynchronous algorithms method, we have constructed a new algorithm that improves the locality of the cross-stencil scheme implementation by the decomposition of the 3D computational domain in time and space. The decomposition is based on a tiling of the 3D1T space into hexahedrons that closely fit the octahedron shape. This shape leads to an algorithm that is less intuitive than the rectangular domain decomposition, but since it follows the natural shape of the dependency region of the cross stencil, it has advantages in data localization and parallelization possibilities. We show its construction, analysis, and implementation possibilities. We present the benchmark results and show that the algorithm follows quantitative estimations: The performance exceeds the memory-bound limit of the stepwise implementation and does not degrade when the whole domain data do not fit higher cache levels.

关键词： LRnLA algorithms Cross stencil parallel algorithms Roofline model

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Real-Time Simulation and Optimization of Elastic Aircraft Vehicle Based on Multi-GPU Workstation

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IEEE ACCESS 2019年 7卷 155659-155670页

作者： Hu, Binxing Li Xingguo Northwestern Polytech Univ Shaanxi Key Lab Aerosp Flight Vehicle Design Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Shaanxi Peoples R China

Modern aircraft such as missile and rocket, due to the large slenderness ratio of slender body vehicles, the influence of elastic deformation and vibration on navigation, guidance, and engine modules in simulation can not be ignored. For the problems of slow calculation speed and incapability of real-time simulation for time-domain simulation, by analyzing the time proportion of each calculation step under different computing scale, the dynamic parallel construction of octree is used to represent the aerodynamic parameter table under the environment of single and multi GPU. Meanwhile, an innovative parallel algorithm of element stiffness matrix based on finite element model is designed in GPU architecture. Accordingly, the optimized performance is enhanced through the adaptive hardware resources and rational use of shared memory. Furthermore, A multi-threaded asynchronous framework based on task queue and thread pool is proposed to realize the parallel task calculation with different granularities. The numerical result shows that the acceleration ratio of about 20 times in the single GPU condition can be obtained, and the acceleration ratio of at least 30 times can be obtained by the parallel computing of dual GPUs, enabling the real-time simulation of the flexible aircraft with 1200 elements within 20ms.

关键词： Vibrations Aircraft Computational modeling Aerodynamics Atmospheric modeling Graphics processing units Mathematical model Flexible aircraft parallel computing parallel algorithms heterogeneous computing Octree

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LocalBoost: A parallelizable Approach to Boosting Classifiers

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NEURAL PROCESSING LETTERS 2019年第1期50卷 19-41页

作者： Valle, Carlos Nanculef, Ricardo Allende, Hector Moraga, Claudio Univ Tecn Federico Santa Maria Dept Informat Valparaiso Chile Univ Playa Ancha Dept Comp & Informat Valparaiso Chile Dortmund Univ D-44221 Dortmund Germany

Ensemble learning is an active field of research with applications to a broad range of problems. Adaboost is a widely used ensemble approach, however, its computational burden is high because it uses an explicit diversity method for building the individual learners. To address this issue, we present a variant of Adaboost where the learners can be trained in parallel, exchanging information on a sparse collaborative communication that restricts the visibility among them. Experiments on 12 UCI datasets show that this approach is competitive in terms of generalization error but more efficient than Adaboost and two other parallel approximations of this algorithm.

关键词： Adaboost Ensemble learning Classification parallel algorithms Learning architectures

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A novel multi-core algorithm for frequent itemsets mining in data streams

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PATTERN RECOGNITION LETTERS 2019年 125卷 241-248页

作者： Bustio-Martinez, Lazaro Munoz-Briseno, Alfredo Cumplido, Rene Hernandez-Leon, Raudel Feregrino-Uribe, Claudia A. Natl Inst Astrophys Opt & Elect Luis Enrique Erro 1 Puebla 72840 Mexico Adv Technol Applicat Ctr 7a 21406 E 214&216 Havana 12200 Cuba

Data streams are modern data sources that are gaining attention as a consequence of their many practical applications (they can be found in data transmission, eCommerce, and intrusion detection system among others). Nevertheless, the efforts to obtain insights from data streams are limited due to their massive information volume and the time needed to process them. In this paper, a new approach for Frequent Itemsets Mining on data streams based on prefix trees which takes advantage of multi-core systems is proposed. This approach uses the Gearman framework as the interface for multi-core processing, and it allows to exploit their scalability efficiently. Experimental results show that the proposed method obtains the same patterns compared with similar approaches reported in the state-of-the-art and outperforms them concerning the processing time required. Also, it is proved that the proposed method is insensitive to variations in the support threshold value, and its efficiency depends on the size of the transactions and not on the size of the alphabet, which is a significant issue in other Frequent Itemsets Mining algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Frequent itemsets mining Data streams Lexicographic order Gearman parallel algorithms

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An efficient automatic multiple objectives optimization feature selection strategy for internet text classification

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第5期10卷 1151-1163页

作者： Huang, Changqin Zhu, Jia Liang, Yuzhi Yang, Min Fung, Gabriel Pui Cheong Luo, Junyu South China Normal Univ Guangdong Engineeting Res Ctr Smart Learning Guangzhou Guangdong Peoples R China South China Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China Univ Hong Kong Sch Comp Sci Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Sichuan Univ Dept Comp Sci Chengdu Sichuan Peoples R China

Research on feature selection in text classification is usually limited to propose various techniques to select a set of features with highest scores based on different metrics. The selected features are usually determined by using a separate validation dataset with a fixed threshold. Obviously, it may not generalize well to new data as the best number for selected features is various on different datasets. In this paper, we first conduct a deep analysis, and find that simply extracting the features based on the score calculated by a metric may not always be the best strategy as it may turn many documents into zero length, which make them not suitable for training. We then model the feature selection process as a multiple objectives optimization problem to gain the best number of selected features rationally and automatically. In addition, as the optimization process costs a lot of resources, we design a parallel algorithm to improve the running time using dynamic programming. Extensive experiments are performed on several popular datasets, and the results indicate that our proposed approach is effective and feasible.

关键词： parallel algorithms Optimization Procedure Dataset Text categorization new data Metronidazole AUTOMATIC Feature extraction Runtime

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A parallel Blum-Micali Generator Based on the Gauss Periods

A Parallel Blum-Micali Generator Based on the Gauss Periods

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International Symposium on Information Theory and Its Applications, ISITA

作者： Yuta Kodera Tomoya Tatara Takuya Kusaka Yasuyuki Nogami Satoshi Uehara Graduate School of Natural Science and Technology Okayama University Okayama Japan Faculty of Environmental Engineering The University of Kitakyushu Fukuoka Japan

ISBN: (数字)9784885523304

ISBN: (纸本)9781728128559

In this paper, the authors propose an algorithm to generate a sequence of bits in parallel to enhance the generating performance of the Blum-Micali method which is for constructing a pseudorandom number generator. More precisely, since the classical definition needs to refer the previous state to generate the next bit, we modify the generation steps in an alternative way by introducing the feature of the Gauss periods. It gives us a unique representation of elements as if a primitive element generates those elements as the powers and this mechanism realizes the parallel algorithm. As a result, the generator achieves to mimic the main concept of the Blum-Micali method in parallel and it is thought to be secure if the discrete logarithm problem is hard to solve.

关键词： Generators parallel algorithms Information theory

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Exploring Various Levels of parallelism in High-Performance CRC algorithms

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IEEE ACCESS 2019年 7卷 32315-32326页

作者： Chi, Mucong He, Dazhong Liu, Jun Beijing Univ Posts & Telecommun Ctr Data Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

Modern processors have increased the capabilities of instruction-level parallelism (ILP) and thread-level parallelism (TLP). These resources, however, typically exhibit poor utilization on conventional cyclic redundancy check (CRC) algorithms. In this paper, various levels of parallelism in high-performance CRC algorithms are investigated. The main idea of the proposed algorithms is to make full utilization of modern processors, from the perspective of both instruction-level and thread-level parallelism. First, a fine-grained algorithm executes the CRC computation in an interleaved manner, so that multiple independent data flows can be processed simultaneously. This algorithm allows instruction-level parallelism, which triples and doubles the performance of the existing slicing-by-4 and slicing-by-8 algorithms, respectively. Second, a coarse-grained algorithm can ideally deal with data in a parallel way by parallelizing a family of serial CRC generating algorithms. Therefore, this algorithm allows thread-level parallelism, which can make full use of multi-core computing capability. As a result, it achieves a speedup that is almost equal to the number of threads used. In addition, both fine-grained and coarse-grained algorithms can be applied together to achieve high throughput further. (This is an extended version of a paper that appeared at the 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) in Montreal, QC, Canada, in 2017.)

关键词： Cyclic redundancy check (CRC) fault detection parallel algorithms

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