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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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An On-Going Framework for Easily Experimenting with Deep Learning Models for Bioimaging Analysis 15th

An On-Going Framework for Easily Experimenting with Deep Lea...

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15th International conference on distributed Computing and Artificial Intelligence (DCAI)

作者： Garcia, Manuel Dominguez, Cesar Heras, Jonathan Mata, Eloy Pascual, Vico Univ La Rioja Dept Matemat & Computac Edificio CCTMadre Dios 53 Logrono 26006 La Rioja Spain

ISBN: (纸本)9783319996080

Due to the broad use of deep learning methods in Bioimaging, it seems convenient to create a framework that facilitates the task of analysing different models and selecting the best one to solve each particular problem. In this work-in-progress, we are developing a Python framework to deal with such a task in the case of bioimage classification. Namely, the purpose of the framework is to automate and facilitate the process of choosing the best combination of feature extractors (obtained from transfer learning and other techniques), and classification models. The features and models to test are fixed by a simple configuration file to facilitate the use of the framework by non-expert users. The best model is automatically selected through a statistical study, and then it can be employed to predict the category of new images.

关键词： Deep learning Machine learning parallelization Bioimaging image processing

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On Machine Learning-based Stage-aware Performance Prediction of Spark Applications

On Machine Learning-based Stage-aware Performance Prediction...

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

作者： Guangjun Ye Wuji Liu Chase Q. Wu Wei Shen Xukang Lyu School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou China New Jersey Institute of Technology Newark NJ USA School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

The data volume of large-scale applications in various science, engineering, and business domains has experienced an explosive growth over the past decade, and has gone far beyond the computing capability and storage capacity of any single server. As a viable solution, such data is oftentimes stored in distributed file systems and processed by parallel computing engines, as exemplified by Spark, which has gained increasing popularity over the traditional MapReduce framework due to its fast in-memory processing of streaming data. Spark engines are generally deployed in cloud environments such as Amazon EC2 and Alibaba Cloud. However, storage and computing resources in these cloud environments are typically provisioned on a pay-as-you-go basis and thus an accurate estimate of the execution time of Spark workloads is critical to making full utilization of cloud resources and meeting performance requirements of end users. Our insight is that the execution pattern of many Spark workloads is qualitatively similar, which makes it possible to leverage historical performance data to predict the execution time of a given Spark application. We use the execution information extracted from Spark History Server as training data and develop a stage-aware hierarchical neural network model for performance prediction. Experimental results show that the proposed hierarchical model achieves higher accuracy than a holistic prediction model at the end-to-end level, and also outperforms other existing regression-based prediction methods.

关键词： Neural networks Predictive models parallel processing Data models Sparks Servers Engines

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ScaleCom: scalable sparsified gradient compression for communication-efficient distributed training 20

ScaleCom: scalable sparsified gradient compression for commu...

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Proceedings of the 34th International conference on Neural Information processing Systems

作者： Chia-Yu Chen Jiamin Ni Songtao Lu Xiaodong Cui Pin-Yu Chen Xiao Sun Naigang Wang Swagath Venkataramani Vijayalakshmi Srinivasan Wei Zhang Kailash Gopalakrishnan IBM T. J. Watson Research Center Yorktown Heights NY

ISBN: (纸本)9781713829546

Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, numerous gradient compression techniques have been proposed and have demonstrated high compression ratios. However, most existing methods do not scale well to large scale distributed systems (due to gradient build-up) and/or fail to evaluate model fidelity (test accuracy) on large datasets. To mitigate these issues, we propose a new compression technique, Scalable Sparsified Gradient Compression (ScaleCom), that leverages similarity in the gradient distribution amongst learners to provide significantly improved scalability. Using theoretical analysis, we show that ScaleCom provides favorable convergence guarantees and is compatible with gradient all-reduce techniques. Furthermore, we experimentally demonstrate that ScaleCom has small overheads, directly reduces gradient traffic and provides high compression rates (65-400X) and excellent scalability (up to 64 learners and 8-12X larger batch sizes over standard training) across a wide range of applications (image, language, and speech) without significant accuracy loss.

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Accelerating MRI Reconstruction on TPUs

Accelerating MRI Reconstruction on TPUs

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IEEE conference on High Performance Extreme Computing (HPEC)

作者： Tianjian Lu Thibault Marin Yue Zhuo Yi-Fan Chen Chao Ma Google Research Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School

ISBN: (数字)9781728192192

ISBN: (纸本)9781728192208

The advanced magnetic resonance (MR) image reconstructions such as the compressed sensing and subspace-based imaging are considered as large-scale, iterative, optimization problems. Given the large number of reconstructions required by the practical clinical usage, the computation time of these advanced reconstruction methods is often unacceptable. In this work, we propose using Google's Tensor processing Units (TPUs) to accelerate the MR image reconstruction. TPU is an application-specific integrated circuit (ASIC) for machine learning applications, which has recently been used to solve large-scale scientific computing problems. As proof-of-concept, we implement the alternating direction method of multipliers (ADMM) in TensorFlow to reconstruct images on TPUs. The reconstruction is based on multi-channel, sparsely sampled, and radial-trajectory k-space data with sparsity constraints. The forward and inverse non-uniform Fourier transform operations are formulated in terms of matrix multiplications as in the discrete Fourier transform. The sparsifying transform and its adjoint operations are formulated as convolutions. The data decomposition is applied to the measured k-space data such that the aforementioned tensor operations are localized within individual TPU cores. The data decomposition and the inter-core communication strategy are designed in accordance with the TPU interconnect network topology in order to minimize the communication time. The accuracy and the high parallel efficiency of the proposed TPU - based image reconstruction method are demonstrated through numerical examples.

关键词： Tensors Sensitivity Scientific computing Discrete Fourier transforms Sensors Acceleration image reconstruction

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NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.

NeuroKube: An Automated and Autoscaling Neuroimaging Reconst...

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IEEE International conference on Big Data

作者： Matthew Madany Kyle Marcus Steven Peltier Mark H. Ellisman Ilkay Altintas University of California San Diego La Jolla CA USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The Neuroscience domain stands out from the field of sciences for its dependence on the study and characterization of complex, intertwining structures. Understanding the complexity of the brain has led to widespread advances in the structure of large-scale computing resources and the design of artificially intelligent analysis systems. However, the scale of problems and data generated continues to grow and outpace the standards and practices of neuroscience. In this paper, we present an automated neuroscience reconstruction framework, called NeuroKube, for large-scale processing and labeling of neuroimage volumes. Automated labels are generated through a machine-learning (ML) workflow, with data-intensive steps feeding through multiple GPU stages and distributed data locations leveraging autoscalable cloud-native deployments on a multi-institution Kubernetes system. Leading-edge hardware and storage empower multiple stages of machine-learning, GPU-accelerated solutions. This demonstrates an abstract approach to allocating the resources and algorithms needed to elucidate the highly complex structures of the brain. We summarize an integrated gateway architecture, and a scalable workflow-driven segmentation and reconstruction environment that brings together image big data with state-of-the-art, extensible machine learning methods.

关键词： Neuroimaging Neuroscience Machine learning algorithms Machine learning Big Data image reconstruction Standards

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20th International conference on Business Process Modeling, Development and Support, BPMDS 2019 and 24th International conference on Evaluation and Modeling methods for Systems Analysis and Development, EMMSAD 2019

20th International Conference on Business Process Modeling, ...

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20th International conference on Business Process Modeling, Development and Support, BPMDS 2019 and 24th International conference on Evaluation and Modeling methods for Systems Analysis and Development, EMMSAD 2019

ISBN: (纸本)9783030206178

The proceedings contain 24 papers. The special focus in this conference is on Business Process Modeling, Development and Support. The topics include: A Situational Approach to Data-Driven Service Innovation;on the Need for Intellectual Property Protection in Model-Driven Co-Engineering Processes;dynamic Adaptation of Capabilities: Exploring Meta-model Diversity;a Process for Tailoring Domain-Specific Enterprise Architecture Maturity Models;ontology-Driven Enterprise Modeling: A Plugin for the Protégé Platform;review-Based User Profiling: A Systematic Mapping Study;matching Technology with Enterprise Architecture and Enterprise Architecture Management Tasks Using Task Technology Fit;a Method for Database Model Selection;towards Ontological Support for Journalistic Angles;coordinating Large distributed Process Structures;a System for Semi-automatic Construction of image processing Pipeline for Complex Problems;specification and Management of methods - A Case for Multi-level Modelling;the Usage of Constraint Specification Languages: A Controlled Experiment;Dealing with Structural Differences in Serialized BPMN Models;evaluating Usefulness of a Fractal Enterprise Model Experience Report;early Validation Framework for Critical and Complex Process-Centric Systems;logic Based Look-Ahead for the Execution of Multi-perspective Declarative Processes;Exploring the Understandability of a Hybrid Process Design Artifact Based on DCR Graphs;a Landscape for Case Models;testing the Fractal Enterprise Model in Practice: Experience Report;augmented Reality-Based Process Modelling for the Internet of Things with HoloFlows;the Rise of Enforceable Business Processes from the Hashes of Blockchain-Based Smart Contracts.

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MG-Hybrid: A Strongly Connected Components Detection Algorithm using Multiple GPUs

MG-Hybrid: A Strongly Connected Components Detection Algorit...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Junteng Hou Shupeng Wang Guangjun Wu Bingnan Ma Chengxiang Si Siyu Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Detection of strongly connected component (SCC) on the GPU has become a fundamental operation to accelerate graph computing. Existing SCC detection methods on multiple GPUs introduce massive unnecessary data transformation between multiple GPUs. In this paper, we propose a novel distributed SCC detection approach using multiple GPUs plus CPU. Our approach includes three key ideas: (1) segmentation and labeling over large-scale datasets; (2) collecting and merging the segmented SCCs; and (3) running tasks assignment over multiples GPUs and CPU. We implement our approach under a hybrid distributed architecture with multiple GPUs plus CPU. Our approach can achieve device-level optimization and can be compatible with the state-of-the-art algorithms. We conduct extensive theoretical and experimental analysis to demonstrate efficiency and accuracy of our approach. The experimental results expose that our approach can achieves 11.2×, 1.2×, 1.2× speedup for SCC detection using NVIDIA K80 compared with Tarjan's, FB-Trim, and FB-Hybrid algorithms respectively.

关键词： Graphics processing units Detection algorithms image edge detection Task analysis Partitioning algorithms parallel algorithms Electronic mail

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Malaria Detection Using Multiple Deep Learning Approaches 2

Malaria Detection Using Multiple Deep Learning Approaches

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2nd International conference on Intelligent Communication and Computational Techniques (ICCT)

作者： Nayak, Satabdi Kumar, Sanidhya Jangid, Mahesh Manipal Univ Jaipur Dept Informat Technol Jaipur Rajasthan India Manipal Univ Jaipur Dept Comp Sci Jaipur Rajasthan India

ISBN: (纸本)9781728117119

With about 200 million global instances and over 400,000 fatalities a year, malaria continues an enormous strain on global health. Modern information technology plays a major part in many attempts to combat the disease, along with biomedical research and political efforts. In specific, insufficient malaria diagnosis was one of the obstacles to a promising mortality decrease. The paper offers an outline of these methods and explores present advancement in the field of microscopic malaria detection and we have ventured into utilization of deep learning for detection of Malaria Parasite. Deep Learning over the years has proven to be much faster and much more accurate as it automates feature extraction of the dataset. In this research paper, we investigated various models of Deep Learning and monitored which of these models provided a better accuracy and faster resolution than previously used deep learning models. Our results show that Resnet 50 model gave the highest accuracy of 0.975504.

关键词： Malaria detection Deep learning Blood Cell detection Medical image processing

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Brain Tumor Segmentation by Texture Feature Extraction with the parallel Implementation of Fuzzy C-Means using CUDA on GPU 5

Brain Tumor Segmentation by Texture Feature Extraction with ...

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5th IEEE International conference on parallel, distributed and Grid Computing (PDGC)

作者： Saxena, Sanjay Shama, Suraj Int Inst Informat Technol Bhubaneswar IIIT Bhuban Dept Comp Sci & Engn Bhubaneswar India

ISBN: (纸本)9781728106465

Exact segmentation of the brain tumor is one of the imperative tasks in medical image processing and its analysis as it deals with extracting the information of the tumorous region from the brain MRI sequences. Automated segmentation and detection of brain tumors from the brain MRI is an exigent issue caused by the texture, size, shape, and location. In this paper, a significant method of brain tumor segmentation from the FLAIR MRI sequences is by classifying local window followed by parallel fuzzy c means clustering. Fuzzy c- means methods have shown their efficiency in extracting a variety of objects in several medical image processing applications. However, one of the major issues of these algorithms is high computational requirements at the time of dealing with large data set. Nowadays, NVIDIA's GPU plays an extremely essential role in implementing such time-consuming algorithms to reduce the time complexity. Our experiments based on NCI-MICCAI BRATS 2017 FLAIR MRI of HGG (High-Grade Glioma) demonstrate the efficiency of the implemented parallel algorithm. For the segmentation of tumorous region, a mechanism of sliding window is implemented on CPU (host) in which a 45 X 45 sized window is taken to classify whether that particular window is having tumor region or not. For perfect segmentation at the GPU (device) side, fuzzy c means technique is used to get the exact location of the tumor. Approx 17.6 speed up obtained, for the BRATS data sets over the implementation of the algorithm on CPU. Apart from speed up significant dice similarity coefficients are obtained which shows the efficient segmentation in the reasonable time.

关键词： Brain Tumor Segmentation Computed Unified Device Architecture Graphics processing Unit High Performance Computing Texture Feature Extraction Fuzzy C-means

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Rank differences of signals by weighing-selection processing method for implementation of multifunctional image processing processor 4

Rank differences of signals by weighing-selection processing...

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conference on Emerging Imaging and Sensing Technologies for Security and Defence IV part of SPIE Security + Defence Symposium

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, Diana V. Vinnitsa Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnitsa Ukraine Vinnitsa Natl Tech Univ Vinnytsia Ukraine

ISBN: (数字)9781510630307

ISBN: (纸本)9781510630307;9781510630291

In this paper, we proposed a new iterative process of sorting an array of signals, which differs from the known structures of sorting signals by uniformity, versatility, which allows direct and inverse sorting of an array of analog or digital signals. The basic elements of the proposed sorting structures are simple relational nodes. Such elements can be implemented on a different element basis, including, on devices for selecting a maximum or minimum of two analog or digital signals, which are implemented on CMOS current mirrors and carry out the limited difference function of continuous logic. We offered implementation of homogeneous sorting structure on such elements, consisting of two layers and a multichannel sampling and holding device. Nine signals corresponding to a selection window of a matrix sensor are fed to this structure, we sort them in five iterative steps, and at the output we receive the signals sorted by the rank, which, using the code controlled programmable multiplexer, generates an output signal, corresponding to the selected rank. We evaluated the technical parameters of such a relational preprocessor. The base cells consist of no more than 20 CMOS 1.5 mu m transistors, the total power consumption of the sorting node on 10 continuously logical base cells (CL BC) is 2mW, the supply voltage is 1.8 divided by 3.3V, the range of an input photocurrent is 0.1o24 mu A, the conversion cycle is 10 mu s. The paper considers results of design and modeling of CL BC based on current mirrors (CM) for creating picture type image processors (IP) with matrix parallel inputs-outputs. Such sorting nodes based on them have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level. The inclusion of an iterative node for sorting signals into a modified nonlinear IP structure makes it possible to significantly simplify its design and increase the functional capabilities of such processor. The simulation results co

关键词： analog relational preprocessor image nonlinear processing simulation continuous logic current mirror sorting networks methods of selection and rank preprocessing rank filtering

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