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检索条件"任意字段=Workshop on Machine Learning in High-Performance Computing Environments, MLHPC 2015"

共 132 条记录，以下是91-100 订阅

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Topology-Aware GPU Scheduling for learning Workloads in Cloud environments 17

Topology-Aware GPU Scheduling for Learning Workloads in Clou...

引用

International Conference for high performance computing, Networking, Storage and Analysis (SC)

作者： Amaral, Marcelo Polo, Jorda Carrera, David Seelam, Seetharami Steinder, Malgorzata Univ Politecn Cataluna Barcelona Supercomp Ctr Barcelona Spain Barcelona Supercomp Ctr Barcelona Spain IBM Watson Res Ctr Yorktown Hts NY USA

ISBN: (数字)9781450351140

ISBN: (纸本)9781450351140

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments. This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to approximate to 1.30x compared to state-of-the-art strategies;the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a largescale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.

关键词： Scheduling Placement GPU Multi-GPU performance Analysis Resource Contention Workload Interference and Deep learning

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Proceedings of mlhpc 2015: machine learning in high-performance computing environments - Held in conjunction with SC 2015: The International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of MLHPC 2015: Machine Learning in High-Performa...

引用

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

ISBN: (纸本)9781450340069

The proceedings contain 5 papers. The topics discussed include: asynchronous parallel stochastic gradient descent - a numeric core for scalable distributed machine learning algorithms;HPDBSCAN - highly parallel DBSCAN;dynamic adaptive neural network arrays: a neuromorphic architecture;optimizing deep learning hyper-parameters through an evolutionary algorithm;and LBANN: Livermore big artificial neural network HPC toolkit.

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Proceedings of WORKS 2017: 12th workshop on Workflows in Support of Large-Scale Science - Held in conjunction with SC 2017: The International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of WORKS 2017: 12th Workshop on Workflows in Sup...

引用

12th workshop on Workflows in Support of Large-Scale Science, WORKS 2017 - Held in conjunction with the International Conference for high performance computing, Networking, Storage and Analysis, SC 2017

ISBN: (纸本)9781450351294

The proceedings contain 8 papers. The topics discussed include: E-HPC: a library for elastic resource management in HPC environments;on the use of burst buffers for accelerating data-intensive scientific workflows;rvGAHP - push-based job submission using reverse SSH connections;a compiler transformation-based approach to scientific workflow enactment;supporting task-level fault-tolerance in HPC workflows by launching MPI jobs inside MPI jobs;towards preserving results confidentiality in cloud-based scientific workflows;a machine learning approach for modular workflow performance prediction;and processing of crowd-sourced data from an Internet of floating things.

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A Scalable Parallel Q-learning Algorithm for Resource Constrained Decentralized computing environments

A Scalable Parallel Q-Learning Algorithm for Resource Constr...

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2nd workshop on machine learning in HPC environments (mlhpc)

作者： Camelo, Miguel Famaey, Jeroen Latre, Steven Univ Antwerp IMEC Dept Math & Comp Sci Middelheimlaan 1 B-2020 Antwerp Belgium

ISBN: (纸本)9781509038824

The Internet of Things (IoT) is more and more becoming a platform for mission critical applications with stringent requirements in terms of response time and mobility. Therefore, a centralized high performance computing (HPC) environment is often not suitable or simply non-existing. Instead, there is a need for a scalable HPC model that supports the deployment of applications on the decentralized but resource constrained devices of the IoT. Recently, Reinforcement learning (RL) algorithms have been used for decision making within applications by directly interacting with the environment. However, most RL algorithms are designed for centralized environments and are time and resource consuming. Therefore, they are not applicable to such constrained decentralized computing environments. In this paper, we propose a scalable Parallel Q-learning (PQL) algorithm for resource constrained environments. By combining a table partition strategy together with a co-allocation of both processing and storage, we can significantly reduce the individual resource cost and, at the same time, guarantee convergence and minimize the communication cost. Experimental results show that our algorithm reduces the required training in proportion of the number of Q-learning agents and, in terms of execution time, it is up to 24 times faster than several well-known PQL algorithms.

关键词： Internet of things

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Behavioural analytics using process mining in on-line advertising

Behavioural analytics using process mining in on-line advert...

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2017 ICCBR workshops on Computational Analogy and Case-Based Reasoning, CAW 2017, Case-Based Reasoning and Deep learning, CBRDL 2017 and Process-Oriented Case-Based Reasoning, POCBR 2017, Doctoral Consortium, and Competitions, ICCBR-WS 2017

作者： Diapouli, Maria Kapetanakis, Stelios Petridis, Miltos Evans, Roger School of Computing Engineering and Mathematical Sciences University of Brighton Watts Building Lewes Road BrightonBN2 4GJ United Kingdom Department of Computing University of Middlesex United Kingdom

Online behavioural targeting is one of the most popular business strategies on the display advertising today. It is based primarily on analysing web user behavioural data with the usage of machine learning techniques with the aim to optimise web advertising. Being able to identify "unknown" and "first time seen" customers is of high importance in online advertising since a successful guess could identify "possible prospects" who would be more likely to purchase an advertisement's product. By identifying prospective customers, online advertisers may be able to optimise campaign performance, maximise their revenue as well as deliver advertisements tailored to a variety of user interests. This work presents a hybrid approach benchmarking machine-learning algorithms and attribute preprocessing techniques in the context of behavioural targeting in process oriented environments. The performance of our suggested methodology is evaluated using the key performance metric in online advertising which is the predicted conversion rate. Our experimental results indicate that the presented process mining framework can significantly identify prospect customers in most cases. Our results seem promising, indicating that there is a need for further workflow research in online display advertising. Copyright © 2017 for this paper by its authors.

关键词： Behavioral research

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A Study of Complex Deep learning Networks on high performance, Neuromorphic, and Quantum Computers

A Study of Complex Deep Learning Networks on High Performanc...

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2nd workshop on machine learning in HPC environments (mlhpc)

作者： Potok, Thomas E. Schuman, Catherine D. Young, Steven R. Patton, Robert M. Spedalieri, Federico Liu, Jeremy Yao, Ke-Thia Rose, Garrett Chakma, Gangotree Oak Ridge Natl Lab 1 Bethel Valley Rd Oak Ridge TN 37830 USA USC Informat Sci Inst Marina Del Rey CA USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9781509038824

Current Deep learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a lowpower hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases;a high performance computer can find optimal layer-based topologies;and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

关键词： Quantum computers

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A Scalable Parallel Q-learning Algorithm for Resource Constrained Decentralized computing environments

A Scalable Parallel Q-Learning Algorithm for Resource Constr...

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workshop on machine learning in HPC environments (mlhpc)

作者： Miguel Camelo Jeroen Famaey Steven Latré University of Antwerp-imec Antwerp Belgium

关键词： Convergence Training Partitioning algorithms Heuristic algorithms Algorithm design and analysis Scalability machine learning algorithms

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A Study of Complex Deep learning Networks on high performance, Neuromorphic, and Quantum Computers

A Study of Complex Deep Learning Networks on High Performanc...

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workshop on machine learning in HPC environments (mlhpc)

作者： Thomas E. Potok Catherine D. Schuman Steven R. Young Robert M. Patton Federico Spedalieri Jeremy Liu Ke-Thia Yao Garrett Rose Gangotree Chakma Oak Ridge National Laboratory USC Information Sciences Institute Marina del Rey CA USA University of Tennessee Knoxville TN USA

ISBN: (纸本)9781509038831

Current Deep learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and *** this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our *** results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive *** represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

关键词： machine learning Quantum computing Computers Neuromorphics Network topology Topology Computer architecture

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An Adaptive Resource Provisioning Method Using Job History learning Technique in Hybrid Infrastructure 1

An Adaptive Resource Provisioning Method Using Job History L...

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1st IEEE International workshop on Foundations and Applications of Self-* Systems (FAS-W)

作者： Choi, Jieun Kim, Yoonhee Sookmyung Womens Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781509036516

Cloud computing technology enables scientists to dynamically expand their environments for scientific experiments. However, to maximize performance and satisfy user requirements it is difficult to quickly provide hybrid resources suitable to application characteristics. In this paper, we design a resource provisioning model based on application characteristic profiles and job history analysis in hybrid computing infrastructure consisting of cluster and cloud environments. In addition to the multi-layer perceptron machine learning method, error back-propagation technique is used to analyze job history to re-learn the error of the output value. Also, we propose an adaptive resource provisioning method for horizontal/vertical scaling of VMs in accordance with the state of the system. We experiment CPU-intensive applications according to the proposed model and algorithms, in a hybrid infrastructure. The experimental results show that using the proposed method, we satisfy user-specified SLA (cost and execution time) and improve the efficiency of resource usage.

关键词： Adaptive resource provisioning multi-layer perceptron learning job history hybrid infrastructure cloud

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Asynchronous parallel stochastic gradient descent: A numeric core for scalable distributed machine learning algorithms

Asynchronous parallel stochastic gradient descent: A numeric...

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

作者： Keuper, Janis Pfreundt, Franz-Josef Fraunhofer ITWM Competence Center High Performance Computing Kaiserslautern Germany

ISBN: (纸本)9781450340069

The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of convergence and accuracy. Recently, several parallelization approaches have been proposed in order to scale SGD to solve very large ML problems. At their core, most of these approaches are following a MapReduce scheme. This paper presents a novel parallel updating algorithm for SGD, which utilizes the asynchronous single-sided communication paradigm. Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent (ASGD) provides faster convergence, at linear scalability and stable accuracy. © 2015 ACM.

关键词： MapReduce

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