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IEEE 14th International Symposium on parallel and distributed Computing (ISPDC)

作者： Kouge, Hiroaki Ito, Yasuaki Nakano, Koji Hiroshima Univ Dept Informat Engn Kagamiyama 1-4-1 Higashihiroshima 7398527 Japan

ISBN: (纸本)9781467371483

The main contribution of this paper is to show a new GPU implementation for the digital halftoning by the local exhaustive search that can generate high quality binary images. We have considered programming issues of the GPU architecture to implement these two methods on the GPU. The experimental result shows that our GPU implementation for the local exhaustive search on NVIDIA GeForce GTX 980 for a 512x512 gray scale image runs in 732 seconds, while the CPU implementation runs in 37,364 seconds. Thus, our GPU implementation attains a speed-up factor of 50.98. Additionally, we also propose a GPU implementation for the digital halftoning by the partial exhaustive search of which the search space of the local exhaustive search is reduced. Similarly, we can accelerate the computation of the partial exhaustive search 30.73 times faster.

关键词： image processing Digital halftoning GPGPU Local exhaustive search Partial exhaustive search

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Teaching Heart Modeling and Simulation on parallel Computing Systems

Teaching Heart Modeling and Simulation on Parallel Computing...

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21st International Conference on parallel and distributed Computing (Euro-Par)

作者： Sozykin, Andrey Chernoskutov, Mikhail Koshelev, Anton Zverev, Vladimir Ushenin, Konstantin Solovyova, Olga Inst Math & Mech UrB RAS Ekaterinburg Russia Ural Fed Univ Ekaterinburg Russia Inst Immunol & Physiol UrB RAS Ekaterinburg Russia

ISBN: (纸本)9783319273082;9783319273075

High Performance Computing (HPC) is an interdisciplinary field of study, which requires learning a number of topics, including not only parallel programming, but also numerical methods and domain science. Stand-alone parallel computing courses are insufficient for thorough HPC education. We present an interdisciplinary track of coherent courses devoted to modeling and simulation of the heart on parallel computing systems for master students at the Ural Federal University. The track consists of three modules: parallel and distributed computing, heart modeling, and numerical methods. Knowledge of numerical methods and heart modeling provides the students with the ability to acquire profound parallel programming skills by working out on the comprehensive programming assignment and complex heart modeling projects. Interdisciplinary approach also increases students' motivation and involvement.

关键词： High performance computing distributed computing HPC education Heart simulation Living system simulation

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Enabling factor analysis on thousand-subject neuroimaging datasets

Enabling factor analysis on thousand-subject neuroimaging da...

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IEEE International Conference on Big Data

作者： Michael J. Anderson Mihai Capota Javier S. Turek Xia Zhu Theodore L. Willke Yida Wang Po-Hsuan Chen Jeremy R. Manning Peter J. Ramadge Kenneth A. Norman Parallel Computing Lab Intel Corporation Hillsboro Oregon Princeton University Princeton New Jersey Dartmouth College Hanover New Hampshire

ISBN: (纸本)9781467390064

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and the Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99χ and 2062x speedups on the two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5χ respectively with 20 nodes on real datasets. We demonstrate weak scaling on a synthetic dataset with 1024 subjects, equivalent in size to the biggest fMRI dataset collected until now, on up to 1024 nodes and 32,768 cores.

关键词： Optimization Computational modeling Neuroimaging Brain modeling Algorithm design and analysis Data models

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Map-Reduce Based Framework for Instrument Detection in Large-Scale Surgical Videos

Map-Reduce Based Framework for Instrument Detection in Large...

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International Conference on Control Communication & Computing India (ICCC)

作者： Sunny, Basil C. Ramesh, R. Varghese, Abraham Vazhayil, Vikas ASIET Dept CSE Ernakulam Kerala India NIMHANS Dept Neurosurg Bangalore Karnataka India

ISBN: (纸本)9781467373494

Modern digital world produces massive amount of data generally refereed as Big Data, which play important roles in dictating the quality of our lives. Relationships among such data have high value, but extremely complex task to establish. Medical Field is one of the major big data sources which produces big volume of data. Modern surgical tools have the capability to record High Definition(HD) videos during the surgical procedure which enables post surgical reviews. Such tools produces giga bytes(GB) of video footage after every surgery which needs mass storage and complex processing. A major solution for this problem is parallel distributed processing using Hadoop based Map Reduce Framework. This paper proposes a Surgical Video Analysis Framework using Hadoop to analyze large surgical videos, for identifying surgical instruments used. Framework first converts videos into large number of frames and using Hadoop image Process Interface (HIPI) it is converted to HIB image bundles. parallel processing of images in the bundle is done by mappers and identified instrument frame information's are logged. Three different feature extraction methods: Scale-Invariant Feature Transform(SIFT), Speeded Up Robust Features(SURF) with Support Vector Machines(SVM) and Haralick Texture Descriptor with Support Vector Machines(SVM) is used in mappers for local image processing.

关键词： Hadoop Mapreduce HIPI Big data distributed processing

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A Low-Power Edge Detection image Sensor Based on parallel Digital Pulse Computation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS ii-EXPRESS BRIEFS 2015年第11期62卷 1043-1047页

作者： Lee, Changhyuk Chao, Wei Lee, Sunwoo Hone, James Molnar, Alyosha Hong, Sang Hoon Cornell Univ Dept Elect & Comp Engn Ithaca NY 14853 USA Kyung Hee Univ Dept Elect & Radio Engn Yongin 446701 South Korea Columbia Univ Dept Mech Engn New York NY 10027 USA

An all-digital low-power CMOS edge detection image sensor array is presented. Each pixel contains a voltage-controlled ring oscillator to achieve low-power and cost-efficient digital-only edge detection. While conventional edge detection methods require high computing power and large chip area to process intensity maps, this work implements an all-digital parallel processing algorithm that detects differences between neighboring pixel pairs on chip, hence reducing the aforementioned power and cost overheads. In particular, a simple column-shared frequency comparator enables low-power operation by eliminating arithmetic computations with large memory requirement. Such a simple edge detection algorithm allows the processor area to be less than 16% of the entire image sensor, therefore maximizing the proportion of active optical area. The prototype image sensor presented in this work is fabricated using a four-metal 180-nm CMOS image sensor process and contains 105 x 92 pixels. An individual pixel size is 8 x 8 mu m(2) with a fill factor of 11.69%, while the total chip area is 1 x 1.3 mm(2). The image sensor exhibits a frame rate of 30 frames/s and a power consumption of 8 mW, which is 27.7 nW/pixel/frame at V-DD of 1.6 V.

关键词： CMOS image sensor edge detection pulse computation voltage-controlled ring oscillator (VCRO)

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INFORMATION-ENTROPY BASED LOAD BALANCING IN parallel ADAPTIVE VOLUME RENDERING

INFORMATION-ENTROPY BASED LOAD BALANCING IN PARALLEL ADAPTIV...

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International Conference on Interfaces and Human Computer Interaction / International Conference on Game and Entertainment Technologies / International Conference on Computer Graphics, Visualization, Computer Vision and image processing

作者： Wang, Huawei Ai, Zhiwei Cao, Yi Inst Appl Phys & Computat Math 2 Fenghao East Rd Beijing 100094 Peoples R China

ISBN: (纸本)9789898533388

Aiming at TB-scale time-varying scientific datasets, this paper presents a novel static load balancing scheme based on information entropy to enhance the efficiency of the parallel adaptive volume rendering algorithm. An information-theory model is proposed firstly, and then the information entropy is calculated for each data patch, which is taken as a pre-estimation of the computational amount of ray sampling. According to their computational amounts, the data patches are distributed to the processing cores balancedly, and accordingly load imbalance in parallel rendering is decreased. Compared with the existing methods such as random assignment and ray estimation, the proposed entropy-based load balancing scheme can achieve a rendering speedup ratio of 1.23 similar to 2.84. It is the best choice in interactive volume rendering due to its speedup performance and view independence.

关键词： Load Balancing Information Entropy parallel Volume Rendering View Independence

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分布式深度学习框架的优化方法

分布式深度学习框架的优化方法

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作者：漆鹏中国科学院信息工程研究所

学位级别：硕士

深度学习技术是目前计算机科学中最火的技术，在图像识别、语音处理等应用领域中取得了巨大的成果。随着深度学习技术的推广与应用，越来越多的深度学习框架涌现出来，例如Caffe、TensorFlow等。这些深度学习框架简单易用，为深度学习... 详细信息

深度学习技术是目前计算机科学中最火的技术，在图像识别、语音处理等应用领域中取得了巨大的成果。随着深度学习技术的推广与应用，越来越多的深度学习框架涌现出来，例如Caffe、TensorFlow等。这些深度学习框架简单易用，为深度学习技术相关工作人员提供了极大的便利与帮助。然而随着深度学习模型与训练数据集规模的日益增长，单机深度学习框架难以快速地完成模型训练工作。因此深度学习框架开始支持分布式训练。这类框架采用模型并行的训练模式以处理更大规模的模型，改进训练方法以提高模型的训练效率。通过对分布式深度学习框架中模型并行与训练方法现状进行研究，本文总结发现: 模型并行的训练模式中尚无通用的模型划分方法，主要以人工经验划分为主，模型并行的加速比较低；同时新近提出的随机拟牛顿法存在步长选择困难的问题，容易导致模型训练不稳定，鲁棒性低。针对这两类问题，本文提出两个研究目标： 1. 设计一种通用的模型划分方法以提高模型并行的加速比； 2. 设计一种自适应的随机拟牛顿法，解决其步长选择困难问题，以提高鲁棒性。根据两个研究目标，本文主要做了以下三方面的工作：首先，通过结合两个研究目标与现有的分布式深度学习框架优点，本文设计了一种基于参数服务器的分布式深度学习框架。该框架是由参数服务器和Worker Group两层结构组成，其中参数服务器存储全局参数，Worker Group为实际的模型训练单元。在模型训练过程中Worker Group需要同参数服务器进行全局参数同步。通过分析全局参数同步的通信特点，本文提出了一种贪心全局参数同步通信策略。其次，针对模型并行中人工经验划分困难的问题，本文以流水线加速为出发点，提出了一个模型划分的最优化问题，采用动态规划方法完成对该最优化问题的求解。为了获取最优化问题求解中的输入信息，本文结合硬件情况预估了训练过程中深度学习模型的计算量与内存占用情况。实验表明该模型划分方法较人工经验划分方法具有更高的加速比。最后，本文通过分析随机拟牛顿法中不稳定的原因，设计了一种自适应的步长选择方法。在此基础上，提出了一种自适应的随机拟牛顿方法。本文理论证明了该方法具有全局收敛性，并分析出其鲁棒性高的原因。通过实验验证了该方法在凸优化与非凸优化的两类问题上训练效率高，鲁棒性。

关键词：深度学习框架模型并行随机拟牛顿法收敛分析同步通信策略

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Ensemble-based factor analysis of survey data on computer science courses

Ensemble-based factor analysis of survey data on computer sc...

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International Congress on image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Shaohong Zhang Jiqiao Li Hai Huang Hongqiu Wu Haodong Lin Wenxiao Qiu Department of Computer Science Guangdong University of Education Guangzhou China

ISBN: (纸本)9781509037117

Analysis of course survey data are foundationally important because it has a close relation with students' skills training and talent development. However, there are multiple categories of classification information that characterized different students from different perspectives, such as genders, classes, types or regions. Compared to the correlation between survey data with individual kind of classification, consensus between those with multiple categories is more informative. However, traditional methods can not handle this problem with multiple kinds of classification categories. In view of the problem above, in this work, we proposed to conduct a new factor analysis method in the consensus fashion. Specifically, we computed a consensus structure of different categories of classifications and the similarity matrices for each factor, respectively. Motivated by the famous clustering measure RAND, we proposed a generalized method called Rmm which is used to compute the consistency of two different similarity matrices, with the option to adopt weights. Followed the similar spirit of Rmm, we also computed pairwise consistency between different factors without category information. Experimental results show that: (i) Our Rmm method is capable to combine different categories of classification information in an effective manner;(ii) our Rmm method preserve promising linear property for the combination of classifications; and (iii) Our Rmm method can be used in the case of fuzzy similarity, which is more applicable in different scenarios. With the proposed method, this work attempted to figure out the veiled laws and covered from read data, with the hope to provide suggestions for course arrangement and personnel cultivating program in universities, and to shed light on personal development of university students who major on computer science.

关键词： Education Computer science Data mining Data warehouses distributed databases Data collection

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Automatic Composition of Heterogeneous Models Based on Semantic Web Services

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2015年第3期43卷 339-358页

作者： Huang, Hui He, Ligang Chen, Xueguang Yu, Minghui Wang, Zhiwu Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Peoples R China Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Henan Inst Engn Dept Comp Sci & Engn Zhengzhou 451191 Peoples R China

As an important function of a distributed decision support system, model composition aims to aggregate model functions to solve complex decision problems. Most existing methods on model composition only apply to the models which have the same type of input and output data so that they can be linked together directly. Those methods are inadequate for the heterogeneous models, since a heterogeneous model may have different types of input and output data that are represented in either qualitative or quantitative manner. This paper aims to address the problem of heterogeneous model composition by employing the techniques based on semantic web services and artificial intelligence planning. In this paper, the heterogeneous model composition problem is converted to the problem of planning in nondeterministic domains under partial observability. An automatic composition method is presented to generate the composite model based on the planning as model checking technique. The experiment results are also presented in this paper to show the feasibility and capability of our approach in dealing with the complex problems involving heterogeneous models.

关键词： Decision support systems Model management distributed model composition Planning as model checking Semantic web service

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ObsCon: Integrated Monitoring and Control for parallel, Real-time Applications

ObsCon: Integrated Monitoring and Control for Parallel, Real...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Nussbaum, Alan Choodamani, Shwetha Mathangi Chandra Schwan, Karsten Georgia Inst Technol Coll Comp Georgia Tech Res Inst Atlanta GA 30332 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

ISBN: (纸本)9781467365987

A large class of emerging compute-intensive applications demand real-time or near real-time processing guarantees on streaming data. Sensor processing in particular, has stringent latency requirements for carrying out its digital processing for rapidly incoming radar data streams. The consequent demands on the cluster middleware used to run such codes include (i) efficient online observation of current application performance, coupled with (ii) highly responsive controllers able to dynamically adjust the application's input- and data-dependent runtime behavior. We present the Obs(erver)Con(troller) software for online monitoring and control, which based on specifications of acceptable application states and tunable knobs within the execution environment, ensures that application performance falls within acceptable limits. ObsCon topologies are dynamic, making possible the runtime association of ObsCon methods with arbitrary DAG-structured, distributed/parallel stream processing applications running on high end cluster machines. This paper describes the ObsCon software and its 'grey box' use with a high performance cluster code that exports to ObsCon select `hooks' for online monitoring and control Adaptive Digital Beamforming for a phase-array radar system.

关键词： Application Tuning Monitoring Closed-loop control Middleware Real-time Systems distributed Architectures

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