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Atomic Red-Black distributed Gauss-Seidel Routing Algorithm for Wireless Sensor Networks

Atomic Red-Black Distributed Gauss-Seidel Routing Algorithm ...

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international conference on Networking and Network applications (NaNA)

作者： Ren-Song Ko Department of Computer Science and Information Engineering National Chung Cheng University Chia-Yi Taiwan

ISBN: (纸本)9781467398046

The scalability challenge of many problems in massively-dense wireless sensor networks may be mitigated from a macroscopic perspective. One example is to formulate the optimal load-balancing routing problem as a set of partial differential equations (PDEs) by ignoring microscopic details, the solutions of the PDEs can be then used to route information. Hence, a routing algorithm, the distributed Gauss-Seidel iteration (DGSI) algorithm, was proposed to coordinate sensors to solve the PDEs iteratively. The later improved algorithm, the distributed Multiplicative Schwarz routing (DMSR) algorithm, eliminates the early termination problem of DGSI due to the presence of holes, and thus improves the accuracy of numerical solutions. In this paper, we propose the atomic red-black distributed Gauss-Seidel routing (ARB-DGSR) algorithm to further improve DGSI and DMSR. In addition to a simpler one-phase coordination mechanism, ARB-DGSR allows the values of unknowns to be updated in the red-black order to achieve the maximum degree of parallelism and thus reduce the convergence time. Our simulation results reveal that ARB-DGSR significantly improves the parallelism without too much sacrifice of accuracy.

关键词： Routing Sensors Convergence Wireless sensor networks Frequency division multiplexing Partial differential equations parallel processing

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An approach to improving the performance of CUDA in virtual environment

An approach to improving the performance of CUDA in virtual ...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Shenquan Han Zhou Lei Wenfeng Shen Shengbo Chen Huiran Zhang Tao Zhang Baoyu Xu School of Computer Engineering and Science Shanghai University Shanghai P. R. China

The GPU pass-through technology, under the virtual machine(VM), is always used in CUDA programming. The data will transfer between the VM main memories and GPU memory during the processing of CUDA programs. It is known that the transmission speed of the pinned memory is faster than the pageable when a large amount of data appears. However, the cost of pinned memory will occupy a lot of physical memory and lead to a lack of memory for the VM. So it can also affect the memory usage of other applications in the VM. In this paper, we propose a method to improve the performance of CUDA in virtual environment. In our experiment, the virtual machine manager is chosen as Kernel-based Virtual Machine(KVM), and the GPU pass-through is used for distributing the GPU for the VMs. We also defined a new module as CUDA Memory Management(CMM) which is added into the virtual environment. The result shows that the approach has high efficiency in CUDA program. And the efficiency of data transmission for using the CMM module is more than 16% than the pageable memory only using mode.

关键词： Graphics processing units Virtualization Memory management Coordinate measuring machines Data communication Cloud computing Virtual machining

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GPU acceleration for Bayesian control of Markovian genetic regulatory networks

GPU acceleration for Bayesian control of Markovian genetic r...

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IEEE EMBS international conference on Information Technology applications in Biomedicine (ITAB)

作者： He Zhou Jiang Hu Sunil P. Khatri Frank Liu Cliff Sze Mohammadmahdi R. Yousefi Department of Electrical and Computer Engineering Texas A&M University IBM Austin Research Laboratory Department of Electrical and Computer Engineering Ohio State University

ISBN: (纸本)9781509024568

A recently developed approach to precision medicine is the use of Markov Decision Processes (MDPs) on Gene Regulatory Networks (GRNs). Due to very limited information on the system dynamics of GRNs, the MDP must repeatedly conduct exhaustive search for a non-stationary policy, and thus entails exponential computational complexity. This has hindered its practical applications to date. With the goal of overcoming this obstacle, we investigate acceleration techniques, using the Graphic processing Unit (GPU) platform, which allows massive parallelism. Our GPU-based acceleration techniques are applied with two different MDP approaches: the optimal Bayesian robust (OBR) policy and the forward search sparse sampling (FSSS) method. Simulation results demonstrate that our techniques achieve a speedup of two orders of magnitude over sequential implementations. In addition, we present a study on the memory utilization and error trends of these techniques.

关键词： Graphics processing units Acceleration Markov processes Bayes methods parallel processing Uncertainty Robustness

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Machine Learning Approach for the Predicting Performance of SpMV on GPU

Machine Learning Approach for the Predicting Performance of ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Akrem Benatia Weixing Ji Yizhuo Wang Feng Shi School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781509053827

Sparse Matrix-Vector Multiplication (SpMV) kernel dominates the computing cost in numerous scientific applications. Many implementations based on different sparse formats were proposed recently for optimizing this kernel on the GPU side. Since the performance of the SpMV varies significantly according to the sparsity characteristics of the input matrix and the hardware features, developing an accurate performance model for this kernel is a challenging task. The traditional approach of building such models by analytical modeling is difficult in practice and requires a thorough understanding of the interaction between the GPU hardware and the sparse code. In this paper, we propose to use a machine learning approach to predict the performance of the SpMV kernel using several sparse formats (COO, CSR, ELL, and HYB) on GPU. We used two popular machine learning algorithms, Support Vector Regression (SVR) and Multilayer Perceptron neural network (MLP). Our experimental results on two different GPUs (Fermi GTX 512 and Maxwell GTX 980 Ti) show that the SVR models deliver the best accuracy with average prediction error ranging between 7% and 14%.

关键词： Sparse matrices Graphics processing units Kernel Machine learning algorithms Computational modeling Arrays Adaptation models

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Energy Efficient techniques using FFT for Deep Convolutional Neural Networks

Energy Efficient Techniques using FFT for Deep Convolutional...

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international conference on Advanced Technologies for Communications (ATC)

作者： Nhan Nguyen-Thanh Han Le-Duc Duc-Tuyen Ta Van-Tam Nguyen Univ Paris Saclay CNRS LTCI Telecom ParisTech F-75013 Paris France Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781509027118

Deep convolutional neural networks (CNNs) has been developed for a wide range of applications such as image recognition, nature language processing, etc. However, the deployment of deep CNNs in home and mobile devices remains challenging due to substantial requirements for computing resources and energy needed for the computation of high-dimensional convolutions. In this paper, we propose a novel approach designed to minimize energy consumption in the computation of convolutions in deep CNNs. The proposed solution includes (i) an optimal selection method for Fast Fourier Transform (FFT) configuration associated with splitting input feature maps, (ii) a reconfigurable hardware architecture for computing high-dimensional convolutions based on 2D-FFT, and (iii) an optimal pipeline data movement scheduling. The FFT size selecting method enables us to determine the optimal length of the split input for the lowest energy consumption. The hardware architecture contains a processing engine (PE) array, whose PEs are connected to form parallel flexible-length Radix-2 single-delay feedback lines, enabling the computation of variable-size 2D-FFT. The pipeline data movement scheduling optimizes the transition between row-wise FFT and column-wise FFT in a 2D-FFT process and minimizes the required data access for the element-wise accumulation across input channels. Using simulations, we demonstrated that the proposed framework improves the energy consumption by 89.7% in the inference case.

关键词： Computer architecture Hardware Radio frequency Pipelines Clocks Delays Neural networks Fast Fourier transform pipeline Computer Architecture Neural network Timing devices radiofrequency Computer hardware energy consumption delay piping selection methods

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parallel computation of k-nearest neighbor joins using MapReduce

Parallel computation of k-nearest neighbor joins using MapRe...

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IEEE international conference on Big Data

作者： Wooyeol Kim Younghoon Kim Kyuseok Shim Seoul National University Hanyang University

ISBN: (纸本)9781467390064

The k-nearest neighbor (kNN) join has recently attracted considerable attention due to its broad applications. However, processing fcNN joins is very expensive due to the quadratic nature of the join operation. Furthermore, since there is an increasing trend of applications to deal with big data, computing fcNN joins becomes more challenging. In order to process such big data, parallel and distributed computing using MapReduce recently have received a lot of attention. In this paper, we propose the efficient parallel algorithm KNN-MR to process the fcNN joins using MapReduce. To reduce not only the computational cost of fcNN joins but also the network cost of communicating across machines, we develop the novel vector projection pruning which enables us to identify non-fcNN points that are guaranteed not to be included in the result of a fcNN join. Our performance study confirms the effectiveness and scalability of the proposed algorithm.

关键词： Partitioning algorithms Big data Algorithm design and analysis Approximation algorithms Clustering algorithms parallel algorithms Market research

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ATLAS Eventlndex monitoring system using the Kibana analytics and visualization platform 17

ATLAS Eventlndex monitoring system using the Kibana analytic...

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17th international Workshop on Advanced Computing and Analysis techniques in Physics Research, ACAT 2016

作者： Barberis, D. Cárdenas Zárate, S.E. Favareto, A. Fernandez Casani, A. Gallas, E.J. Garcia Montoro, C. Gonzalez De La Hoz, S. Hrivnac, J. Malon, D. Prokoshin, F. Salt, J. Sanchez, J. Toebbicke, R. Yuan, R. Università di Genova and INFN Genova Italy Universidad Técnica Federico Santa María Valparaíso Chile Università degli Studi and INFN Genova Italy Instituto de Física Corpuscular Valencia Spain Department of Physics Oxford University Oxford United Kingdom Laboratoire de l'Accélérateur Linéaire Orsay France Argonne National Laboratory Argonne United States CERN Europe France

The ATLAS EventIndex is a data catalogue system that stores event-related metadata for all (real and simulated) ATLAS events, on all processing stages. As it consists of different components that depend on other applications (such as distributed storage, and different sources of information) we need to monitor the conditions of many heterogeneous subsystems, to make sure everything is working correctly. This paper describes how we gather information about the EventIndex components and related subsystems: the Producer-Consumer architecture for data collection, health parameters from the servers that run EventIndex components, EventIndex web interface status, and the Hadoop infrastructure that stores EventIndex data. This information is collected, processed, and then displayed using CERN service monitoring software based on the Kibana analytic and visualization package, provided by CERN IT Department. EventIndex monitoring is used both by the EventIndex team and ATLAS distributed Computing shifts crew.

关键词： distributed computer systems

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Performance prediction techniques for scalable large data processing in distributed MPI systems

Performance prediction techniques for scalable large data pr...

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IEEE international conference on Performance, Computing and Communications (IPCCC)

作者： Janki Bhimani Ningfang Mi Miriam Leeser Northeastern University

ISBN: (纸本)9781509052530

Predicting performance of an application running on parallel computing platforms is increasingly becoming important due to the long development time of an application and the high resource management cost of parallel computing platforms. However, predicting overall performance is complex and must take into account both parallel calculation time and communication time. Difficulty in accurate performance modeling is compounded by myriad design choices along multiple dimensions, namely (i) process level parallelism, (ii) distribution of cores on multi-processor platforms, (iii) application related parameters, and (iv) characteristics of datasets. This research proposes a fast and accurate performance prediction approach to predict the calculation and communication time of an application running on a distributed computing platform. The major contribution of our prediction approach is that it can provide an accurate prediction of execution times for new datasets which have much larger sizes than the training datasets. Our approach consists of two models, i.e., a probabilistic self-learning model to predict calculation time and a simulation queuing model to predict network communication time. The combination of these two models provides data analysts a useful insight of optimal configuration of parallel resources (e.g., number of processes and number of cores) and application parameters setting.

关键词： Computational modeling Predictive models Data models Markov processes Data processing Hardware

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Radiative Heat Transfer Calculation on 16384 GPUs Using a Reverse Monte Carlo Ray Tracing Approach with Adaptive Mesh Refinement

Radiative Heat Transfer Calculation on 16384 GPUs Using a Re...

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IEEE international Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Alan Humphrey Daniel Sunderland Todd Harman Martin Berzins Scientific Computing and Imaging Institute University of Utah Salt Lake City UT USA Sandia National Laboratories Albuquerque NM USA Department of Mechanical Engineering University of Utah Salt Lake City UT USA

ISBN: (纸本)9781509036837

Modeling thermal radiation is computationally challenging in parallel due to its all-to-all physical and resulting computational connectivity, and is also the dominant mode of heat transfer in practical applications such as next-generation clean coal boilers, being modeled by the Uintah framework. However, a direct all-to-all treatment of radiation is prohibitively expensive on large computers systems whether homogeneous or heterogeneous. DOE Titan and the planned DOE Summit and Sierra machines are examples of current and emerging GPU-based heterogeneous systems where the increased processing capability of GPUs over CPUs exacerbates this problem. These systems require that computational frameworks like Uintah leverage an arbitrary number of on-node GPUs, while simultaneously utilizing thousands of GPUs within a single simulation. We show that radiative heat transfer problems can be made to scale within Uintah on heterogeneous systems through a combination of reverse Monte Carlo ray tracing (RMCRT) techniques combined with AMR, to reduce the amount of global communication. In particular, significant Uintah infrastructure changes, including a novel lock and contention-free, thread-scalable data structure for managing MPI communication requests and improved memory allocation strategies were necessary to achieve excellent strong scaling results to 16384 GPUs on Titan.

关键词： Computational modeling Graphics processing units Mathematical model Heat transfer Computer architecture Boilers Message systems

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parallel Bin-Occupancy Filter for Image Observation

Parallel Bin-Occupancy Filter for Image Observation

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Proceedings of the Digital Image Computing: Technqiues and applications (DICTA)

作者： Ran Zhu Yunli Long Jungang Yang Wei An National University of Defense Technology Changsha Hunan CN

We present a recursive track-before-detect (TBD) algorithm based on the bin-occupancy filter for multi-target tracking from image observations by asking a different question: "Is there a target within a given pixel?" Analytic posterior probability distribution of the bin-state is derived for image measurements under the non-overlapping assumption. Coupled with the intrinsic grid characteristic of its measurement-space, the proposed algorithm can be regarded as a fully discrete version of the traditional probability hypothesis density (PHD) filter for TBD via quantized state-space modeling. parallel grid implementation is also discussed in order to meet the requirement for realtime processing applications. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm for tracking targets with low signal-to- noise ratio (SNR).

关键词： Target tracking Mathematical model Probability distribution Radio frequency Hardware Real-time systems

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