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Automatic I/O scheduling algorithm selection for parallel file systems

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第8期28卷 2457-2472页

作者： Boito, Francieli Zanon Kassick, Rodrigo Virote Navaux, Philippe O. A. Denneulin, Yves Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Grenoble LIG Lab INRIA Grenoble France

This article presents our approach to provide input/output (I/O) scheduling with double adaptivity: to applications and devices. In high-performance computing environments, parallel file systems provide a shared storage infrastructure to applications. In the situation where multiple applications access this shared infrastructure concurrently, their performance can be impaired because of interference. Our work focuses on I/O scheduling as a tool to improve performance by alleviating interference effects. The role of the I/O scheduler is to decide the order in which applications' requests must be processed by the parallel file system's servers, applying optimizations to adjust the resulting access pattern for improved performance. Our approach to improve I/O scheduling results is based on using information from applications' access patterns and storage devices' sensitivity to access sequentiality. We have applied machine learning to provide the ability to automatically select the best scheduling algorithm for each situation. Our approach improves performance by up to 75% over an approach that uses the same scheduling algorithm to all situations, without adaptability. Our results evidence that both aspects - applications and storage devices - are essential to make good scheduling decisions. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： I/O scheduling parallel file systems high-performance computing

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International workshop on Multi-Agent-Based Simulation, MABS 2015

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16th International workshop on Multi-Agent-Based Simulation, MABS 2015

ISBN: (纸本)9783319314464

The proceedings contain 12 papers. The special focus in this conference is on Formalism Coupling in MABS, Verification in MABS, high performance computing for MABS and MABS Applications. The topics include: Extending the Gillespie’s stochastic simulation algorithm for integrating discrete-event and multi-agent based simulation;coupling micro and macro dynamics models on networks;a Monte Carlo model checker for multiagent-based simulations;data driven validation framework for multi-agent activity-based models;GPU environmental delegation of agent perceptions;parallel simulations of the iterated n-player prisoner’s dilemma;a simulation tool for the analysis of river morphosedimentary adjustments;dynamic data-driven experiments in the smart grid domain with a multi-agent platform;optimality and equilibrium of exploration ratio for multiagent learning in nonstationary environments and maximising influence in non blocking cascades of interacting concepts.

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Optimizing deep learning hyper-parameters through an evolutionary algorithm

Optimizing deep learning hyper-parameters through an evoluti...

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workshop on machine learning in high-performance computing environments, mlhpc 2015

作者： Young, Steven R. Rose, Derek C. Karnowski, Thomas P. Lim, Seung-Hwan Patton, Robert M. Oak Ridge National Laboratory MS-6085 PO Box 2008 Oak RidgeTN37831 United States

ISBN: (纸本)9781450340069

There has been a recent surge of success in utilizing Deep learning (DL) in imaging and speech applications for its relatively automatic feature generation and, in particular for convolutional neural networks (CNNs), high accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, Multi-node Evolutionary Neural Networks for Deep learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms. © 2015 ACM.

关键词： Genetic algorithms

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learning Running-time Prediction Models for Gene-Expression Analysis Workflows

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IEEE LATIN AMERICA TRANSACTIONS 2015年第9期13卷 3088-3095页

作者： Monge, D. A. Holec, M. Zelezny, F. Garino, C. G. Natl Univ Cuyo UNCuyo ITIC Res Inst Mendoza Argentina Natl Univ Cuyo UNCuyo Fac Ciencias Exactas & Nat Mendoza Argentina Czech Tech Univ IDA Res Grp Prague Czech Republic

One of the central issues for the efficient management of Scientific workflow applications is the prediction of tasks performance. This paper proposes a novel approach for constructing performance models for tasks in data-intensive scientific workflows in an autonomous way. Ensemble machine learning techniques are used to produce robust combined models with high predictive accuracy. Information derived from workflow systems and the characteristics and provenance of the data are exploited to guarantee the accuracy of the models. A gene-expression analysis workflow application was used as case study over homogeneous and heterogeneous computing environments. Experimental results evidence noticeable improvements while using ensemble models in comparison with single/standalone prediction models. Ensemble learning techniques made it possible to reduce the prediction error with respect to the strategies of a single-model with values ranging from 14.47% to 28.36% for the homogeneous case, and from 8.34% to 17.18% for the heterogeneous case.

关键词： performance Prediction Ensemble learning Workflows Bioinformatics Distributed computing

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Introducing high performance computing to undergraduate students

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Computers in Education Journal 2016年第4期16卷 104-112页

作者： Cui, Suxia Wang, Yonghui Li, Lin Peng, Xiaobo Yalvac, Bugrahan Engineering Department Prairie View A and M University United States Computer Science Department Prairie View A and M University United States Dept. of Teaching Learning and Culture Texas A and M University College Station United States

Recently, President Obama issued an Executive Order to ensure the United States' leadership in computing. Necessary hardware and software design skills should be introduced into university curricula. computing has been advanced to high performance computing (HPC) throughout the past decades. However, undergraduate students are still lacking of experience in how HPC functions especially in minority-serving institutions, because our current computing curricula do not adequately cover HPC contents. To address this problem, a team of faculty members have obtained external funding supports to improve undergraduate computing education through enhanced courses and research opportunities. The goal is to incorporate HPC concepts and training across the computing curricula in multiple disciplines in order to motivate students' interests in computing and improve their problem-solving skills. This three-year project has already finished the second year of implementation. During the first year, a diverse teaching environment was established, including a HPC cluster and embedded HPC platforms. Both platforms supported students' learning and research in parallel programming, embedded systems design, and data cloud. In the second project year, several courses were revised or developed across three departments: Electrical and Computer Engineering, Computer Science, and Engineering Technology. New course materials integrating the parallel and distributed computing concepts were developed and offered to undergraduate students. Project-based learning was introduced into classroom. More advanced concepts, such as computer vision and machine learning were explored by undergraduate students. At the same time, the research results were disseminated in junior and senior level courses. Faculty members applied effective pedagogy to teach new generation computing. For all the classes involved in this project, student surveys were collected to guide future project implementation. This article s

关键词： Parallel programming

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Activity discovering and modelling with labelled and unlabelled data in smart environments

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第14期42卷 5800-5810页

作者： Wen, Jiahui Zhong, Mingyang Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

In the past decades, activity recognition had aroused great interest for the community of context-awareness computing and human behaviours monitoring. However, most of the previous works focus on supervised methods in which the data labelling is known to be time-consuming and sometimes error-prone. In addition, due to the randomness and erratic nature of human behaviours in realistic environments, supervised models trained with data from certain subject might not be scaled to others. Further more, unsupervised methods, with little knowledge about the activities to be recognised, might result in poor performance and high clustering overhead. To this end, we propose an activity recognition model with labelled and unlabelled data in smart environments. With small amount of labelled data, we discover activity patterns from unlabelled data based on proposed similarity measurement algorithm. Our system does not require large amount of data to be labelled while the proposed similarity measurement method is effective to discover length-varying, disordered and discontinuous activity patterns in smart environments. Therefore, our methods yield comparable performance with much less labelled data when compared with traditional supervised activity recognition, and achieve higher accuracy with lower clustering overhead compared with unsupervised methods. The experiments based on real datasets from the smart environments demonstrate the effectiveness of our method, being able to discover more than 90% of original activities from the unlabelled data, and the comparative experiments show that our methods are capable of providing a better trade-off, regarding the accuracy, overhead and labelling efforts, between the supervised and unsupervised methods. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Data mining machine learning Activity recognition Similarity measurement Labelled and unlabelled data Smart environments

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learning fuzzy controllers in mobile robotics with embedded preprocessing

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APPLIED SOFT computing 2015年 26卷 123-142页

作者： Rodriguez-Fdez, I. Mucientes, M. Bugarin, A. Univ Santiago de Compostela Ctr Invest Tecnoloxias Informac CITIUS Santiago De Compostela Spain

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Mobile robotics Quantified Fuzzy Rules Iterative Rule learning Genetic fuzzy system

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Selecting machine learning Algorithms using Regression Models 15

Selecting Machine Learning Algorithms using Regression Model...

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IEEE 15th International Conference on Data Mining workshops (ICDMW)

作者： Doan, Tri Kalita, Jugal Univ Colorado Dept Comp Sci Colorado Springs CO 80907 USA

ISBN: (纸本)9781467384933

In performing data mining, a common task is to search for the most appropriate algorithm(s) to retrieve important information from data. With an increasing number of available data mining techniques, it may be impractical to experiment with many techniques on a specific dataset of interest to find the best algorithm(s). In this paper, we demonstrate the suitability of tree-based multi-variable linear regression in predicting algorithm performance. We take into account prior machine learning experience to construct meta-knowledge for supervised learning. The idea is to use summary knowledge about datasets along with past performance of algorithms on these datasets to build this meta-knowledge. We augment pure statistical summaries with descriptive features and a misclassification cost, and discover that transformed datasets obtained by reducing a high dimensional feature space to a smaller dimension still retain significant characteristic knowledge necessary to predict algorithm performance. Our approach works well for both numerical and nominal data obtained from real world environments.

关键词： Meta-learning regression dimensionality reduction combined metric

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7th USENIX workshop on Hot Topics in Cloud computing, HotCloud 2015

7th USENIX Workshop on Hot Topics in Cloud Computing, HotClo...

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7th USENIX workshop on Hot Topics in Cloud computing, HotCloud 2015

The proceedings contain 21 papers. The topics discussed include: oh flow, are thou happy? TCP send buffer advertising for make benefit of clouds and tenants;optimizing network performance in distributed machine learning;an optimization case in support of next generation NFV deployment;enabling topological flexibility for data centers using OmniSwitch;GreenMap: MapReduce with ultra high efficiency power delivery;the case for the superfluid cloud;CodePlugin: plugging deduplication into erasure coding for cloud storage;and enabling scalable social group analytics via hypergraph analysis systems.

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A New Technique for Restricted Boltzmann machine learning 8

A New Technique for Restricted Boltzmann Machine Learning

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IEEE 8th International Conference on Intelligent Data Acquisition and Advanced computing System-Technology and Applications (IDAACS)

作者： Golovko, Vladimir Kroshchanka, Aliaksandr Turchenko, Volodymyr Jankowski, Stanislaw Treadwell, Douglas Brest State Tech Univ Brest 224017 BELARUS Univ Lethbridge Lethbridge AB T1K 3M4 Canada Warsaw Univ Technol PL-00665 Warsaw Poland Beckon San Mateo CA 94401 USA

ISBN: (纸本)9781467383615

Over the last decade, deep belief neural networks have been a hot topic in machine learning. Such networks can perform a deep hierarchical representation of input data. The first layer can extract low-level features, the second layer can extract high-level features and so on. In general, deep belief neural network represents many-layered perceptron and permits to overcome some limitations of conventional multilayer perceptron due to deep architecture. In this work we propose a new training technique called Reconstruction Error-Based Approach (REBA) for deep belief neural network based on restricted Boltzmann machine. In contrast to classical Hinton's training approach, which is based on a linear training rule, the proposed technique is based on a nonlinear learning rule. We demonstrate the performance of REBA technique for the MNIST dataset visualization. The main contribution of this paper is a novel view on the training of a restricted Boltzmann machine.

关键词： Restricted Boltzmann machine deep learning data visualization machine learning

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