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Advances in distributed computing with modern drug discovery

EXPERT OPINION ON DRUG DISCOVERY 2019年第1期14卷 9-22页

作者： Jesus Banegas-Luna, Antonio Imbernon, Baldomero Llanes Castro, Antonio Perez-Garrido, Alfonso Ceron-Carrasco, Jose Pedro Gesing, Sandra Merelli, Ivan D'Agostino, Daniele Perez-Sanchez, Horacio Univ Catolica Murcia UCAM Bioinformat & High Performance Comp Res Grp BIO H Murcia Spain Univ Notre Dame Ctr Res Comp Notre Dame IN 46556 USA Natl Res Council Italy Inst Biomed Technol Milan Italy Natl Res Council Italy Inst Appl Math & Informat Technol E Magenes Genoa Italy

Introduction: Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.

关键词： Cloud computing computational chemistry distributed computing drug discovery grid computing high-performance computing virtual screening

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MetaLAFFA: a flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline

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BMC BIOINFORMATICS 2020年第1期21卷 471-471页

作者： Eng, Alexander Verster, Adrian J. Borenstein, Elhanan Univ Washington Dept Genome Sci Seattle WA 98195 USA Hlth Canada Food Directorate Bur Food Surveillance & Sci Integrat Ottawa ON K1A 0K9 Canada Tel Aviv Univ Blavatnik Sch Comp Sci IL-6997801 Tel Aviv Israel Tel Aviv Univ Sackler Fac Med IL-6997801 Tel Aviv Israel Santa Fe Inst Santa Fe NM 87501 USA

BackgroundMicrobial communities have become an important subject of research across multiple disciplines in recent years. These communities are often examined via shotgun metagenomic sequencing, a technology which can offer unique insights into the genomic content of a microbial community. Functional annotation of shotgun metagenomic data has become an increasingly popular method for identifying the aggregate functional capacities encoded by the community's constituent microbes. Currently available metagenomic functional annotation pipelines, however, suffer from several shortcomings, including limited pipeline customization options, lack of standard raw sequence data pre-processing, and insufficient capabilities for integration with distributed computing *** we introduce MetaLAFFA, a functional annotation pipeline designed to take unfiltered shotgun metagenomic data as input and generate functional profiles. MetaLAFFA is implemented as a Snakemake pipeline, which enables convenient integration with distributed computing clusters, allowing users to take full advantage of available computing resources. Default pipeline settings allow new users to run MetaLAFFA according to common practices while a Python module-based configuration system provides advanced users with a flexible interface for pipeline customization. MetaLAFFA also generates summary statistics for each step in the pipeline so that users can better understand pre-processing and annotation *** is a new end-to-end metagenomic functional annotation pipeline with distributed computing compatibility and flexible customization options. MetaLAFFA source code is available at https://***/borenstein-lab/MetaLAFFA and can be installed via Conda as described in the accompanying documentation.

关键词： Metagenomics Functional annotation Pipeline distributed computing

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Research on Optimal Allocation of Integrated Resources of Mergers and Acquisitions Based on distributed Cloud computing

Research on Optimal Allocation of Integrated Resources of Me...

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Information and Communication Engineering (JCICE), International Joint Conference on

作者： Lifen Zhou School of Accountancy Guangdong Baiyun University Guangzhou China

ISBN: (数字)9798350388725

ISBN: (纸本)9798350388732

This paper proposes a scheduling algorithm based on fast edge resource placement, so as to further improve the effect of bilateral resource integration of the mergers and acquisitions (M&A). Among them, the random demand model is taken as the basic resource scheduling method, and the algorithm is optimized by introducing edge computing and user random demand, thereby further improving the resource scheduling effect of mergers and acquisitions. The simulation results show that under different merger and acquisition resources, Zipf values, and types of resources, compared with traditional mean-based scheduling algorithms, the scheduling algorithm based on fast edge resource placement have higher resource scheduling benefits and significantly reduced time complexity. Therefore, it can be concluded that the scheduling algorithm based on fast edge resource placement has good performance, and it can carry out resource scheduling with good effect. In addition, applying the proposed algorithm in the actual enterprise M&A scenario can help enterprises to carry out better resource management, which is feasible and has certain practical application value.

关键词： Scheduling algorithms Corporate acquisitions Simulation Optimization methods Scheduling Resource management Time complexity distributed computing Testing Edge computing

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A distributed computing framework for wind speed big data forecasting on Apache Spark

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2020年 37卷

作者： Xu, Yinan Liu, Hui Long, Zhihao Cent South Univ Sch Traff & Transportat Engn Inst Artificial Intelligence & Robot Key Lab Traff Safety TrackMinist Educ Changsha 410075 Hunan Peoples R China

The randomness of the wind speed leads to the intermittency of wind power, which is a challenge to realize wind power energy as reliable and renewable power. The prediction of wind speed time series can promote the use of wind energy. However, the traditional stand-alone methods are unable to meet the requirements of wind speed big data environments. In the study, a hybrid distributed computing framework on Apache Spark is applied for wind speed big data forecasting. Using the distributed computing strategy, the framework can divide the wind speed big data into RDD groups and operate them in parallel. In the framework, a modified wind speed extreme learning machine predictor is built using the distributed computing Process strategy, enhanced by the data decomposition and result reconstruction components on Apache Spark. The experimental results indicate that the proposed distributed computing framework on Spark can forecast wind speed big data in multi-step accurately. Besides, the effectiveness of different components in the framework is verified. It is also proved that the proposed distributed computing framework has a faster computation speed when processing big data, compared to the stand-alone method.

关键词： Wind speed big data forecasting distributed computing Apache Spark Multi-step forecasting

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distributed Quantum computing for Chemical Applications

Distributed Quantum Computing for Chemical Applications

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Quantum computing and Engineering (QCE), IEEE International Conference on

作者： Grier M. Jones Hans-Arno Jacobsen Department of Chemical and Physical Sciences University of Toronto Toronto Ontario Canada The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In recent years, interest in quantum computing has increased due to technological advances in quantum hardware and algorithms. Despite the promises of quantum advantage, the applicability of quantum devices has been limited to few qubits on hardware that experiences decoherence due to noise. One proposed method to get around this challenge is distributed quantum computing (DQC). Like classical distributed computing, DQC aims at increasing compute power by spreading the compute processes across many devices, with the goal to minimize the noise and circuit depth required by quantum devices. In this paper, we cover the fundamental concepts of DQC and provide insight into where the field of DQC stands with respect to the field of chemistry—a field which can potentially be used to demonstrate quantum advantage on noisy-intermediate scale quantum devices.

关键词： Quantum computing Quantum advantage Noise Qubit Hardware Noise measurement distributed computing Chemicals

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Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing

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SUSTAINABLE computing-INFORMATICS & SYSTEMS 2020年 26卷

作者： Bandopadhaya, Shuvabrata Dey, Rajiv Suhag, Ashok BML Munjal Univ Sch Engn & Technol Gurugram India

This paper has proposed an integrated healthcare monitoring solution for the soldiers deployed in adverse environmental conditions, using the internet of things (IoT) with distributed computing. For these soldiers, the health parameters of every individual need to be monitored on a real-time basis and subsequent analysis of the dataset to be made for initiating appropriate medical support with the lowest possible delay. In this paper, a three-layer service-oriented IoT architecture has been proposed where the computational functionalities are distributed among all the layers. The proposed distributed computing mechanism has implemented two levels of filtration of redundant information that belongs to safe soldiers. The first level of filtering is done at the end-node using the Fuzzy classification approach and the second level of filtering is done at the intermediate node using the time-series pattern analysis approach. This layer-wise filtration process results in a reduction in data flooding and computational burden on the cloud due to which system response time improves to suit emergency applications. A prototype has been developed to validate the effectiveness of the proposed solution. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Healthcare monitoring system Internet of things (IoT) distributed computing Fuzzy classification Partten recognistion Long Range wide area network (LoRaWAN)

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Wireless MapReduce distributed computing

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第10期65卷 6101-6114页

作者： Li, Fan Chen, Jinyuan Wang, Zhiying Louisiana Tech Univ Dept Elect Engn Ruston LA 71272 USA Univ Calif Irvine CPCC Irvine CA 92697 USA

Motivated by mobile edge computing and wireless data centers, we study a wireless distributed computing framework where the distributed nodes exchange information over a wireless interference network. Our framework follows the structure of MapReduce. This framework consists of Map, Shuffle, and Reduce phases, where Map and Reduce are computation phases and Shuffle is a data transmission phase. In our setting, we assume that the transmission is operated over a wireless interference network. We demonstrate that, by duplicating the computation work at a cluster of distributed nodes in the Map phase, one can reduce the amount of transmission load required for the Shuffle phase. In this work, we characterize the fundamental tradeoff between computation load and communication load, under the assumption of one-shot linear schemes. The proposed scheme is based on side information cancellation and zero-forcing, and we prove that it is optimal in terms of computation-communication tradeoff. The proposed scheme outperforms the naive TDMA scheme with single node transmission at a time, as well as the coded TDMA scheme that allows coding across data, in terms of the computation-communication tradeoff.

关键词： distributed computing MapReduce wireless interference network interference management

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distributed computing connected components with linear communication cost

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distributed AND PARALLEL DATABASES 2018年第3期36卷 555-592页

作者： Feng, Xing Chang, Lijun Lin, Xuemin Qin, Lu Zhang, Wenjie Yuan, Long Univ Technol Sydney Sydney NSW Australia Univ Sydney Sydney NSW Australia Univ New South Wales Sydney NSW Australia East China Normal Univ Shanghai Peoples R China

The paper studies three fundamental problems in graph analytics, computing connected components (CCs), biconnected components (BCCs), and 2-edge-connected components (ECCs) of a graph. With the recent advent of big data, developing efficient distributed algorithms for computing CCs, BCCs and ECCs of a big graph has received increasing interests. As with the existing research efforts, we focus on the Pregel programming model, while the techniques may be extended to other programming models including MapReduce and Spark. The state-of-the-art techniques for computing CCs and BCCs in Pregel incur O(m x #supersteps) total costs for both data communication and computation, where m is the number of edges in a graph and #supersteps is the number of supersteps. Since the network communication speed is usually much slower than the computation speed, communication costs are the dominant costs of the total running time in the existing techniques. In this paper, we propose a new paradigm based on graph decomposition to compute CCs and BCCs with O(m) total communication cost. The total computation costs of our techniques are also smaller than that of the existing techniques in practice, though theoretically almost the same. Moreover, we also study distributed computing ECCs. We are the first to study this problem and an approach with O(m) total communication cost is proposed. Comprehensive empirical studies demonstrate that our approaches can outperform the existing techniques by one order of magnitude regarding the total running time.

关键词： distributed computing Connected component Biconnected component 2-Edge-connected component

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Circumventing superefficiency: An effective strategy for distributed computing in non-standard problems

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ELECTRONIC JOURNAL OF STATISTICS 2019年第1期13卷 1926-1977页

作者： Banerjee, Moulinath Durot, Cecile Univ Michigan 275 West Hall1085 South Univ Ann Arbor MI 48109 USA Univ Paris Nanterre Modalx F-92001 Nanterre France

We propose a strategy for computing estimators in some nonstandard M-estimation problems, where the data are distributed across different servers and the observations across servers, though independent, can come from heterogeneous sub-populations, thereby violating the identically distributed assumption. Our strategy fixes the super-efficiency phenomenon observed in prior work on distributed computing in (i) the isotonic regression framework, where averaging several isotonic estimates (each computed at a local server) on a central server produces super-efficient estimates that do not replicate the properties of the global isotonic estimator, i.e. the isotonic estimate that would be constructed by transferring all the data to a single server, and (ii) certain types of M-estimation problems involving optimization of discontinuous criterion functions where M-estimates converge at the cube-root rate. The new estimators proposed in this paper work by smoothing the data on each local server, communicating the smoothed summaries to the central server, and then solving a non-linear optimization problem at the central server. They are shown to replicate the asymptotic properties of the corresponding global estimators, and also overcome the super-efficiency phenomenon exhibited by existing estimators.

关键词： Cube-root asymptotics distributed computing isotonic regression local minimax risk superefficiency

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Optimization Of Electric Vehicle Charging Schedule Using distributed Network computing 52

Optimization Of Electric Vehicle Charging Schedule Using Dis...

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52nd North American Power Symposium (NAPS)

作者： Shek, Chak Lam Manoharan, Arun-Kaarthick Aravinthan, Visvakumar Wichita State Univ Dept Elect Engn & Comp Sci Wichita KS 67260 USA

ISBN: (纸本)9781728181929

The significant growth in the number of electric vehicles indicates an increased demand on the power distribution system, specifically on the low-voltage residential network. Without a well-organized schedule for charging electric vehicles, users will typically apply immediate charging upon arrival to home. This may burden the system and may damage power system equipment. To avoid this adverse effect on the system, a process of scheduling electric vehicle charging should be established. This paper proposes a multi-agent based distributed computing process for solving the electric vehicle charge scheduling problem in a secure way that benefits both the customer and the system. This process breaks down the problem into to global and local problem with the former for system objective and the latter for individual vehicle owners' objective. In this work, the local problems are modeled as sub gradient problems that can be solved simultaneously by corresponding agents. The optimality of the sub gradient solutions with respect to global objective are made sure through information sharing between the agents during each iteration. The detailed modeling and implementation of the proposed method along with numerical analysis to demonstrate the effectiveness are presented in the paper.

关键词： decentralized EV charge scheduling multi-agent system distributed computing Consensus Algorithm

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