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An open-source, hardware accelerated industry prototype for multidimensional glyphs 14

An open-source, hardware accelerated industry prototype for ...

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14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image processing 2020, CGVCVIP 2020 and 5th IADIS international conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and 9th IADIS international conference theory and Practice in Modern Computing 2020, TPMC 2020, Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020

作者： Rees, Dylan Laramee, Robert S. Swansea University Swansea United Kingdom University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9789898704214

Data-glyphs are a common, useful metaphor for visualizing multidimensional data. However, to represent a larger number of glyphs requires an increasing amount of computational power to derive the data-dependent geometry of each glyph. We present a novel visualization implementation for computing and rendering customized data-glyphs for an industry application. Our software utilizes the parallel processing power of the Graphics processing Unit (GPU) to enable interactive exploration of thousands of multidimensional data-glyphs. this is achieved by passing normalized data values to the OpenGL geometry shader to construct the geometry of each glyph reflecting the data values. the technique is applicable to a variety of glyph designs and supports fast selection between different designs. We demonstrate this technique to render more than 100,000 glyphs at interactive frame rates. To address the challenge of reproducing the technique, we provide a simplified open-source code example implementation that can be used by anyone, including our industry partner. © Proceedings of the 14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS international conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS international conference theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.

关键词： Graphics processing unit

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GNNear: Accelerating Full-Batch Training of Graph Neural Networks with near-Memory processing 22

GNNear: Accelerating Full-Batch Training of Graph Neural Net...

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Proceedings of the international conference on parallel architectures and Compilation Techniques

作者： Zhe Zhou Cong Li Xuechao Wei Xiaoyang Wang Guangyu Sun Peking University Beijing China Peking University and Alibaba Group Beijing China

ISBN: (纸本)9781450398688

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. the reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) the irregularity of graphs can result in severe resource under-utilization and load-imbalance *** paper presents a GNNear accelerator to tackle these challenges. GNNear adopts a DIMM-based memory system to provide sufficient memory capacity. To match the heterogeneous nature of GNN training, we offload the memory-intensive Reduce operations to in-DIMM Near-Memory-Engines (NMEs), making full use of the high aggregated local bandwidth. We adopt a Centralized-Acceleration-Engine (CAE) to process the computation-intensive Update operations. We further propose several optimization strategies to deal with the irregularity of input graphs and improve GNNear's performance. Comprehensive evaluations on 16 GNN training tasks demonstrate that GNNear achieves 30.8× / 2.5× geomean speedup and 79.6× / 7.3× (geomean) higher energy efficiency compared to Xeon E5-2698-v4 CPU and NVIDIA V100 GPU.

关键词： machine learning near-memory processing graph neural networks domain-specific accelerator

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Investigating Hybrid and Ensemble Deep Learning architectures for Leukemia Detection 22

Investigating Hybrid and Ensemble Deep Learning Architecture...

引用

Proceedings of the 2022 5th international conference on Machine Learning and Natural Language processing

作者： Skyler Kim Dwight Global Online School USA

ISBN: (纸本)9781450399067

this research designed and implemented a leukemia diagnostic system targeted for a real clinical environment based on deep learning approaches. the dataset consists of 15,135 total images. this research develops four independent models (VGG19 1 epoch, VGG19 30 epochs, ResNet50 1 epoch, and ResNet50 30 epochs), two Hybrid models (trained at 1 epoch and 30 epochs), and four Ensemble models (two Ensemble models of VGG19 and ResNet50 and two Ensemble models with an additional Hybrid model). All models are pre-trained on ImageNet. By using transfer learning, the models were fine-tuned (further trained) on the leukemia domain at a much greater speed as the existing layers will have benefitted from the pre-training done on ImageNet. this research indicates that Hybrid models can help improve predictive capabilities by leveraging different feature patterns extracted from running images through two different architectures. Meanwhile, Ensemble models will take the prediction votes from multiple final model outputs to further incorporate different model capabilities and also help generalize. Among all independent models, the best model is ResNet50 30 epochs, which achieved an accuracy of 84%. Among the Hybrid models, the best model is Hybrid 30 epochs, which achieved an accuracy of 84%. Ensemble 4 (Hybrid 30 epochs, VGG19 1 epoch, and ResNet50 1 epoch) achieved an accuracy of 86%, which is 2% better than the second-best model, Ensemble 2. In principle, the diagnostic system developed in this research can be applied in other medical diagnostic applications.

关键词： Hybrid Models Ensemble Models

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Automatic stereo disparity search range detection on parallel computing architectures 17th

Automatic stereo disparity search range detection on paralle...

引用

17th international conference on Image Analysis and Recognition, ICIAR 2020

作者： Perera, Ruveen Low, Tobias University of Southern Queensland Toowoomba Australia

ISBN: (纸本)9783030503468

From the earliest to the state-of-the-art algorithms, stereo depth estimation techniques often require a disparity search range (DSR) value to be chosen manually. However, the optimal DSR varies from one scene to another making the results depend on the operator input and operator having to optimize the configuration by using trial-and-error. In this paper we present a novel technique suitable for parallel computing architectures which detects the optimum DSR for a given scene without requiring operator input or prior knowledge of the scene. Experiments on stereo images from Middlebury, KITTI and Sceneflow bench-mark datasets indicate that our technique can automatically extract a suitable DSR value from different scenes, which leads to better consistency in matching. the technique presented here can be used with existing stereo algorithms to limit the size of the cost volume as it is being built (without requiring pre-processing or operator input). A CUDA based implementation of our method can deliver real-time performance on consumer grade GPUs at high frame rates. © Springer Nature Switzerland AG 2020.

关键词： Graphics processing unit

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Construction and Application of Bilingual Terminology Database of Political Field Based on Chinese–English parallel Corpus 5th

Construction and Application of Bilingual Terminology Databa...

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5th international conference on Intelligent Computing, Communication and Devices, ICCD 2019

作者： Lin, Jiaxin Li, Wei Guangdong University of Finance and Economics Guangzhou510420 China Tongji University Shanghai200082 China

ISBN: (纸本)9789811558863

the establishment of Chinese–English parallel corpus and bilingual terminology database of political literature is not only the effective integration and in-depth processing of political literature but also conducive to the construction of China’s national image, which is an important subject of current academic research. In view of the above issues, this paper summarizes the current situation of the construction and research of the bilingual terminology database of political literature at home and abroad, which reveals that there is still much room for improvement in the construction of the bilingual terminology database of political literature based on parallel corpus at home and abroad. this paper also explores the construction practice of bilingual terminology database of political literature based on a parallel corpus and analyzes the application prospect of the bilingual terminology database of political literature from the teaching application of terminology translation. It is hoped that this study can bring some enlightenment to the construction of digital resources of political discourse in China. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Database systems

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Reconfigurable HW-SW Co-design Platform for Lung Cancer Detection and Classification

Reconfigurable HW-SW Co-design Platform for Lung Cancer Dete...

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international conference on VLSI Design

作者： Ayushparth Sharma Kusum Lata The LNM Institute of Information Technology Jaipur India

High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. this article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. the proposed platform has been implemented on Xilinx Spartan-6 FPGA board and co-simulated with Xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10× higher energy-efficiency compared to serial counterpart and reduced execution period by 70 ×. Clinical validation shows that parallel architecture introduces 5-7 % error in nodule characteristic determination in comparison to serial one.

关键词： Memory management Lung cancer Very large scale integration Streaming media Energy efficiency Real-time systems parallel architectures

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Cloud Based Voting System Using Blockchain Technology

Cloud Based Voting System Using Blockchain Technology

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Harshul Dudeja Jai Kaushik Shalu Department of Computer Science and Technology Manav Rachna University Faridabad India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Cloud-based voting system employing blockchain technology utilizes the decentralized and encrypted characteristics of blockchain to ensure the legitimacy of election process. Using a cloud-based platform, voters can cast ballots which are then stored on a blockchain network. A cryptographic hash of the prior block and a timestamp are included in each block, rendering the chain resilient. this study examines the prospects for creating a cloud-based voting system using blockchain technology. To improve the security and dependability of electronic voting systems, this manuscript highlighted the perks of blockchain technology, such as decentralization, transparency, and immutability. the study also highlights the difficulties and threats of adopting blockchain technology for voting systems, such as scalability assaults, or reliance on dubious mechanisms, and resistance to the coercion. the research emphasizes the necessity for substantial and in-depth investigations to solve these difficulties and hazards. To address the drawbacks of the current electronic voting systems and guarantee the security, dependability, and integrity of the voting process, deeper analysis and development is required. the emerging world with the advancement of technology is moving towards cloud-based digitization rapidly due to which the need of cloud security is rising dramatically. this research demonstrates the immense potential of blockchain technology for online voting. A safe and transparent voting system can be created by utilizing the advantages of blockchain technology, fostering faith and confidence in the democratic process.

关键词： Resistance Electric potential Electronic voting systems Scalability parallel processing Rendering (computer graphics) Hazards

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8th international conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

8th International Conference on Scale Space and Variational ...

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8th international conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

ISBN: (纸本)9783030755485

the proceedings contain 45 papers. the special focus in this conference is on Scale Space and Variational Methods in Computer Vision. the topics include: Multiscale Registration;challenges for Optical Flow Estimates in Elastography;an Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation;low-Rank Registration of Images Captured Under Unknown, Varying Lighting;towards Efficient Time Stepping for Numerical Shape Correspondence;first Order Locally Orderless Registration;first-Order Geometric Multilevel Optimization for Discrete Tomography;bregman Proximal Gradient algorithms for Deep Matrix Factorization;Hessian Initialization Strategies for -BFGS Solving Non-linear Inverse Problems;inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting;quantisation Scale-Spaces;A Scaled and Adaptive FISTA Algorithm for Signal-Dependent Sparse Image Super-Resolution Problems;Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to parallel MRI;wasserstein Generative Models for Patch-Based Texture Synthesis;sketched Learning for Image Denoising;Translating Numerical Concepts for PDEs into Neural architectures;CLIP: Cheap Lipschitz Training of Neural Networks;variational Models for Signal processing with Graph Neural Networks;synthetic Images as a Regularity Prior for Image Restoration Neural Networks;geometric Deformation on Objects: Unsupervised Image Manipulation via Conjugation;learning Local Regularization for Variational Image Restoration;Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations;on the Correspondence Between Replicator Dynamics and Assignment Flows;learning Linear Assignment Flows for Image Labeling via Exponential Integration;on the Geometric Mechanics of Assignment Flows for Metric Data Labeling;a Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration.

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ParaCA: A Speculative parallel Crawling Approach on Apache Spark 20th

ParaCA: A Speculative Parallel Crawling Approach on Apache S...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Li, Yuxiang Zhang, Zhiyong Niu, DanMei Jing, Junchang Henan Univ Sci & Technol Luoyang 471023 Henan Peoples R China Henan Int Joint Lab Cyberspace Secur Applicat Luoyang 471023 Henan Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

the World Wide Web today is growing at a phenomenal rate. the crawling approach is of vital importance to improve the efficiency of crawling the web. the existing sequent crawling algorithms are mostly time consuming and do not support large data well. In order to improve parallelism and efficiency of crawler on distributed network environments, based on the software thread-level speculation technique, this paper raises a speculative parallel crawler approach (ParaCA) on Apache Spark. By analyzing the process of web crawler, the ParaCA firstly hires a function to divide a crawling process into several sub-processes which can be implemented independently and then spawns a number of threads to speculatively crawl in parallel. At last, the speculative results are merged to form the final outcome. Comparing with the conventional parallel approach on multicore platform, ParaCA is very efficiency and obtains a high parallelism degree by making the best of the resources of the cluster. Experiments show that the proposed approach could achieve a significant speedup improvement with compare to the traditional approach in average. In addition, with the growing number of working nodes, the execution time decreases gradually, and the speedup scales linearly. the results indicate that the crawling efficiency can be significantly enhanced by adopting this speculative parallel algorithm.

关键词： Crawling approach Speculative multithreading Apache Spark

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Deep Learning Based parallel Detector for MIMO Systems 5

Deep Learning Based Parallel Detector for MIMO Systems

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5th international conference on Communication, Image and Signal processing (CCISP)

作者： Huang, Di Jiang, Xue-Qin Chen, Sijing Wu, Yun Bai, Enjian Donghua Univ Sch Informat Sci & Technol Shanghai Peoples R China Wuhan Maritime Commun Res Inst Wuhan Peoples R China

ISBN: (纸本)9781728185897

Multi-input multi-output (MIMO) technology becomes one of the most popular technologies for the fifth-generation (5G) wireless networks due to the advantages in terms of spectrum efficiency and data rate. However, some existing MIMO detectors cannot achieve a good trade-off between the performance and computational complexity. In this paper, we propose a novel DL-based detector for signal detection in MIMO systems, which integrates several modified DNNs into a parallel structure. By changing the connection between layers of conventional deep neural network (DNN) detectors and introducing some differences into these modified DNNs, the diversity of the detection results of these modified DNNs can be achieved. the numerical results verify that our proposed DL-based detector can obtain better performance in MIMO systems compared with some existing linear detectors and conventional DNN detector.

关键词： MIMO systems deep learning neural networks detection

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