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18th International Conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Wu, Changmao Zhang, Changyou Sun, Qiao Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Monte-Carlo rendering algorithms are known for producing highly realistic images, but at a significant computational cost, because they rely on tracing up to trillions of light paths through a scene to simulate physically based light transport. For this reason, a large body of research exists on various techniques for accelerating these costly algorithms. As one of the Monte-Carlo rendering algorithms, PSSMLT (Primary Sample Space Metropolis Light Transport) is widely used nowadays for photorealistic rendering. Unfortunately, the computational cost of PSSMLT is still very high since the space of light paths in high-dimension and up to trillions of paths are typically required in such path space. Recent research on PSSMLT has proposed a variety of optimized methods for single node rendering, however, multi-node rendering for PSSMLT is rarely mentioned due in large part to the complicated mathematical model, complicated physical processes and the irregular memory access patterns, and the imbalanced workload of light-carrying paths. In this paper, we present a highly scalable distributed parallel simulation framework for PSSMLT. Firstly, based on light transport equation, we propose the notion of sub-image with certain property for multi-node rendering and theoretically prove that the whole set of sub-images can be combined to produce the final image;Then we further propose a sub-image based assignment partitioning algorithm for multi-node rendering since the traditional demand-driven assignment partitioning algorithm doesn't work well. Secondly, we propose a physically based parallel simulation for the PSSMLT algorithm, which is revealed on a parallel computer system in master-worker paradigm. Finally, we discuss the issue of granularity of the assignment partitioning and some optimization strategies for improving overall performance, and then a static/dynamic hybrid scheduling strategy is described. Experiments show that framework has a nearly linear speedup along wi

关键词： Primary sample space metropolis light transport Physically based ray tracing Assignment partitioning Hybrid scheduling distributed computing Performance optimization

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DEEP image CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK 25

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

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25th IEEE International Conference on image processing (ICIP)

作者： Wang, Qiang Xu, Jiaqing Li, Rongchun Qiao, Peng Yang, Ke Li, Shijie Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479970612

image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Deep Clustering Convolutional Autoencoders Kullback-Leibler divergence

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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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Investigation of image processing techniques for glaucoma detection in human eyes 5

Investigation of image processing techniques for glaucoma de...

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5th International Conference on parallel, distributed and Grid Computing, PDGC 2018

作者： Kaushal, Shilpa Datt Sharma, Sunil Jain, Shruti Dept. of Electronics Comm. Engg. Jaypee University of Information Technology Distt. SolanHimachal Pradesh India

ISBN: (纸本)9781538660263

Glaucoma is a disease associated with retina of eye. Presently, millions of human being is suffering from this disease. Early detection of these diseases can save the people from blindness. Therefore, various methods have been developed for its detection. In this paper, we have studied the reported methods and summarized their performance in terms of accuracy of detection. © 2018 IEEE.

关键词： Ophthalmology

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An improved distributed storage and query for remote sensing data

An improved distributed storage and query for remote sensing...

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6th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Jing, Weipeng Tian, Dongxue Northeast Forestry Univ Coll Informat & Comp Engn Harbin 150040 Heilongjiang Peoples R China

With the rapid development of information technology, the amount of remote sensing data is increasing at an unprecedented scale. In the presence of massive remote sensing data, the traditional processing methods have the problems of low efficiency and lack of scalability, so this paper uses open source big data technology to improve it. Firstly, the storage model of remote sensing image data is designed by using the distributed storage database HBase. Then, the grid index and the Hibert curve are combined to establish the index for the image data. Finally, the method of MapReduce parallel processing is used to write and query remote sensing images. The experimental results show that the method can effectively improve the data writing and query speed, and has good scalability. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： remote data distribute storage data query HBase mapreduce

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GPU-Accelerated Light-field image Super-resolution

GPU-Accelerated Light-field Image Super-resolution

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International Conference on Advanced Computing and Applications (ACOMP)

作者： Trung-Hieu Tran Mammadov, Gasim Sun, Kaicong Simon, Sven Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781538691861

Light-field imaging has become an emerging technology that brings great benefits to many fields, i.e. in photography, academia, and industry. However, these benefits come with the cost of high computation requirement that limits its applications in practice. This paper presents an accelerated solution for 4D light-field image super-resolution. The acceleration is achieved by the mean of parallel computation using graphics processing units. The selected algorithm is broken into functions which is suitable for parallel execution. Each of the functions is then transformed into GPU kernel and executed at each work-item which is associated with a pixel location in the proposed architecture. Using disparity maps extracted from input 4D light-field as an aid for super-resolution task, the proposed approach can successfully super-resolute an input 4D light-field by the factor of 4 horizontally and vertically. Two strategies, Y-RGB and RGB, are proposed to handle color images. Y-RGB is suitable for high-speed processing constraints while RGB is more preferable if output quality is the main concern. Experimental results show that the proposed approach can achieve the speed up of 203x and 71x compared to CPU implementation for Y-RGB and RGB strategy respectively. Regarding output quality, the proposed approach generates a shaper high-resolution image with more details compared to the baseline methods.

关键词： Spatial resolution Graphics processing units Imaging Estimation Computer architecture Three-dimensional displays

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A Memory Efficient parallel Particle-Based Volume Rendering for Large-Scale distributed Unstructured Volume Datasets in HPC Environments 18th

A Memory Efficient Parallel Particle-Based Volume Rendering ...

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18th Annual Asia Simulation Conference (AsiaSim)

作者： Yamaoka, Yoshiaki Hayashi, Kengo Sakamoto, Naohisa Nonaka, Jorji Kobe Univ Kobe Hyogo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9789811328534;9789811328527

In recent years, the size and complexity of the datasets generated by the large-scale numerical simulations using modern HPC (High Performance Computing) systems have continuously increasing. These generated datasets can possess different formats, types, and attributes. In this work, we have focused on the large-scale distributed unstructured volume datasets, which are still applied on numerical simulations in a variety of scientific and engineering fields. Although volume rendering is one of the most popular techniques for analyzing and exploring a given volume data, in the case of unstructured volume data, the time-consuming visibility sorting becomes problematic as the data size increases. Focusing on an effective volume rendering of large-scale distributed unstructured volume datasets generated in HPC environments, we opted for using the well-known PBVR (Particle-based Volume Rendering) method. Although PBVR does not require any visibility sorting during the rendering process, the CPU-based approach has a notorious image quality and memory consumption tradeoff. This is because that the entire set of the intermediate rendering primitives (particles) was required to be stored a priori to the rendering processing. In order to minimize the high pressure on the memory consumption, we propose a fully parallel PBVR approach, which eliminates the necessity for storing these intermediate rendering primitives, as required by the existing approaches. In the proposed method, each set of the rendering primitives is directly converted to a partial image by the processes, and then they are gathered and merged by the utilized parallel image composition library (234Compositor). We evaluated the memory cost and processing time by using a real CFD simulation result, and we could verify the effectiveness of our proposed method compared to the already existing parallel PBVR method.

关键词： Particle-based Volume Rendering Unstructured volume dataset 234 image composition In-situ visualization

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Fast Nonlocal Diffusion by Stacking Local Filters for image Denoising

Fast Nonlocal Diffusion by Stacking Local Filters for Image ...

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2019国际计算机前沿大会

作者： Peng Qiao Weichu Sun Yong Dou Rongchun Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology University of South China

Many image denoising methods achieve state-of-the-art performance with little consideration of computation efficiency. With the popularity of high-definition imaging devices, these denoising methods do not scale well to the high-definition images. Therefore, a fast image denoising method is proposed to meet the demand of processing highdefinition images. Based on the analysis of the distribution of the distance distrs between the similar patches and their reference patches from a semantic aspect, the large distrss was found to occur while their contribution to the overall distribution was small. Therefore, the nonlocal filters was replaced in trainable non-local reaction diffusion(TNLRD)with local filters. For image with 4096 × 4096 resolution, the proposed method runs about 6 times faster than TNLRD via a single-thread CPU implementation. And the GPU implementation of the proposed method is about 10 times faster than the CPU implementation. Furthermore,the proposed model achieves competing denoising performance compared with TNLRD in terms of PSNR and SSIM.

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A Spectral Radius Based Evaluation Function for Automatic Focusing

A Spectral Radius Based Evaluation Function for Automatic Fo...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Guojin Peng Meini Lv Kuikui Wang Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University WuZhou china

ISBN: (纸本)9781728152103

Curves generated by traditional evaluating functions contain large amount of local extremums, easily leading to focal-plane misjudgments. We propose a new clarity evaluation method based on spectral radius. The spatial information is converted into frequency domain by Discrete Cosine Transformation, then a sector that contains more than 99 percent of the total spectrum energy is extracted, and the radius of this sector is employed to estimate the degree of clarity of current image. Since the scope of spectral radius is steady and limited to image size, it provides effective reference for selection of lower limit of spectral radius which is utilized to avoid local fluctuation and maintain the unimodality of evaluation curves. The experimental results demonstrate that the spectral radius based evaluation method forms curves with fewer local extremums and better robustness.

关键词： discrete cosine transforms gradient methods image recognition spectral radius discrete cosine transforms gradient methods image recognition Curves Clarity Current Pick image SIZE Sectoring SPECTRAL ENERGY evaluation method

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Scalable deep learning on distributed infrastructures: Challenges, techniques and tools

arXiv

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arXiv 2019年

作者： MAYER, RUBEN JACOBSEN, HANS-ARNO Technical University of Munich Boltzmannstr. 3 Garching85748 Germany

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-art results in various domains such as image recognition and natural language processing. One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available. To keep on improving the performance of DL, increasing the scalability of DL systems is necessary. In this survey, we perform a broad and thorough investigation on challenges, techniques and tools for scalable DL on distributed infrastructures. This incorporates infrastructures for DL, methods for parallel DL training, multitenant resource scheduling and the management of training and model data. Further, we analyze and compare 11 current open-source DL frameworks and tools and investigate which of the techniques are commonly implemented in practice. Finally, we highlight future research trends in DL systems that deserve further research. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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