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Recognition of micro-objects with adaptive models of image processing in a parallel computing environment

Journal of Physics: conference Series 2021年第1期1791卷

作者： I I Jumanov O I Djumanov R A Safarov Samarkand State University 15 University Boulevard Samarkand Uzbekistan

Scientific and methodological foundations for optimizing the processing of micro-objects, in particular, pollen grains, have been developed on the basis of models and methods of preliminary information processing with mechanisms for filtering, identifying, and using textural, specific characteristics, and geometric features of images. The efficiency of image filtering mechanisms is investigated based on the use of statistical control rules, adaptive two-threshold control, trend functions, and control of the contour description error, traditional detectors of random noise, and interference. The mechanisms of image identification using biquadratic, orthogonal algebraic polynomials, 4th order splines are proposed. A technique for optimizing image identification by finding a function by its integrals in a family of straight lines based on the Mellin and Fourier transforms has been developed. Combined models with mechanisms for smoothing reference points, segmentation of contours, filtering with detection of changes in dynamics, optimization by the conjugate gradient method, time relaxation have been built. A software complex for the recognition and classification of images of pollen grains in C ++ has been developed, which includes testing identification algorithms based on the Daubechies spline function, orthogonal algebraic polynomials of 4, 8 orders in the parallel computing environment 'CUDA'.

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A user profile analysis framework driven by distributed machine learning for big data 2019

A user profile analysis framework driven by distributed mach...

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2019 International conference on Artificial Intelligence and Computer Science, AICS 2019

作者： Wang, Xiaodong Wang, Qing Tao, Ye Department of Computer and Technology Ocean University of China Qingdao China Department of Computer Science and Technology Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9781450371506

In recent years, big data has become the new focus of attention from all walks of life. The valuable information contained in big data becomes the driving force for people to process and analyze big data. Big data analytics helps enterprises to take better decisions to improve business output. As a user description tool, user profile is widely used in various fields. However, it is difficult to deal with large-scale datasets using traditional methods since the established processes was not designed to handle large volumes of data. In this paper, we propose a user profile analysis framework using machine learning approach which apply advanced machine learning programs to solve industrial scale problems. And this approach can be effective to speculate real and potential needs of various groups of users and precisely extract individual characteristics and group generality. By introducing high-level data parallel framework, the process of large-scale data processing can be executed efficiently. We use real-world data to validate the effectiveness of the proposed framework. © 2019 Association for Computing Machinery.

关键词： User profile

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Markov Processes in image processing

Markov Processes in Image Processing

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International conference on Information Technologies in Business and Industry

作者： Petrov, E. P. Kharina, N. L. Vyatka State Univ 36 Moskovskaya St Kirov 610000 Russia

Digital images are used as an information carrier in different sciences and technologies. The aspiration to increase the number of bits in the image pixels for the purpose of obtaining more information is observed. In the paper, some methods of compression and contour detection on the basis of two-dimensional Markov chain are offered. Increasing the number of bits on the image pixels will allow one to allocate fine object details more precisely, but it significantly complicates image processing. The methods of image processing do not concede by the efficiency to well-known analogues, but surpass them in processing speed. An image is separated into binary images, and processing is carried out in parallel with each without an increase in speed, when increasing the number of bits on the image pixels. One more advantage of methods is the low consumption of energy resources. Only logical procedures are used and there are no computing operations. The methods can be useful in processing images of any class and assignment in processing systems with a limited time and energy resources.

关键词： Energy resources

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Building a Cloud-Ready Program: A highly scalable Implementation based on Kubernetes 18

Building a Cloud-Ready Program: A highly scalable Implementa...

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2nd International conference on Advances in image processing (ICAIP) / 2nd International conference on Software Engineering and Development (ICSED

作者： Li, Qiankun Yin, Gang Wang, Tao Yu, Yue Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

ISBN: (纸本)9781450364607

Build system, which can convert source codes into applications, is essential for the development of software. The general build systems that relying on single physical or cloud host to run bring problems such as system security, resource shortage, overload, and low availability in the face of massive build requests. After modularizing and streamlining the steps during a build process, this paper proposes a system that introduces container technology and then builds a large-scale, real-time, and huge-concurrency supported build system based on Kubernetes[1]. The system provides a highly scalable and feature-stable cloud architecture that supports huge concurrency with lower resource consumption. Also, the system controls programs' behaviors very well to avoid potential security and resource issues and shows excellent performance in concurrency, scalability, security, and load balance even when handling a large number of build tasks.

关键词： Build system Container Kubernetes Concurrent build

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Stingray-HPC: A Scalable parallel Seismic Raytracing System 26

Stingray-HPC: A Scalable Parallel Seismic Raytracing System

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26th Euromicro International conference on parallel, distributed, and Network-Based processing (PDP)

作者： Monil, Mohammad Alaul Haque Malony, Allen D. Toomey, Doug Huck, Kevin Univ Oregon Dept Comp & Informat Sci Eugene OR 97403 USA Univ Oregon Dept Earth Sci Eugene OR 97403 USA Univ Oregon Performance Res Lab Eugene OR 97403 USA

ISBN: (纸本)9781538649756

The Stingray raytracer was developed for marine seismology to compute minimum travel time from all sources in an earth model to determine the 3D geophysical structure below the ocean floor. The original sequential implementation of Stingray used Dijkstra's single-source, shortest-path (SSSP) algorithm. A data parallel version of Stingray was developed based on the Bellman-Ford-Moore iterative SSSP algorithm. Single node experiments demonstrated performance improvements from parallelization with multicore (using OpenMP) and manycore processors (using CUDA). Calculating seismic ray paths for larger earth models requires distributed, multi-node algorithms utilizing domain decomposition methods. Preliminary 2D decomposition strategies show promising scaling results. However, a general 3D decomposition methodology is needed to handle any seismic raytracing problem on any HPC computing platform. In this paper, we present Stingray-HPC, a framework for scalable seismic raytracing which can automatically decompose a 3D earth model across nodes in a distributed environment, allocate ghost cell regions for iterative updates, coordinate ghost cell communications, and test for global convergence. Stingray-HPC is implemented with MPI and either OpenMP or CUDA for node-level calculations. Our results validate Stingray-HPC's ability to handle large models (over a billion points) and to solve these models efficiently at scale up to 512 GPU nodes.

关键词： marine seismology raytracing tomography shortest path GPU

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Role of Mathematics in image processing

Role of Mathematics in Image Processing

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International conference on Machine Learning, Big Data, Cloud and parallel Computing (COMITCon)

作者： Manisha Vashisht Madhulika Bhatia Manav Racha International Institute of Research and Studies Faridabad India

ISBN: (纸本)9781728102122

The various image processing technologies have revolutionized the way in which a particular image is captured, stored, or retrieved from a certain source. Most commonly used image processing tasks include; image Restoration, image Segmentation, image Enhancement, De-Blurring, De-noising, etc. Such images are well utilized in various ways by several of the Imaging techniques, like; Angiography, MRI (Magnetic Resonance Imaging), Arterial Spin Labeling (ASL), Computer Assisted Tomography (CT), Deep Brain Stimulation (DBS), Electroencephalography (EEG), etc. But, it is the, Mathematics that has played a crucial role in various image processing tasks mentioned above. Despite the innovation and rapid advances in the various Imaging Technology, one thing that has remained important throughout, is, the use of *** has been observed that image processing has got strong connection with Mathematics. Many of the image processing methods rely on the basic Mathematical Techniques of Histogram Equalization, Probability and Statistics, Discrete Cosine Transforms, Fourier Transforms, Differential Equations, Integration, Matrix and Algebra. For computational purposes, Matlab is one of the most commonly used tool by the researchers in the area of image processing. Other Tools which are commonly used include SciLab, GNU Octave, SageMath, *** paper critically reviews the role of Mathematics in various image processing Applications. This paper would also present the basic Mathematical tools and techniques, used in image processing, along with the literature review of the same.

关键词： Discrete cosine transforms image edge detection Tools Laplace equations Maximum likelihood detection

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distributed parallel Simulation of Primary Sample Space Metropolis Light Transport 18th

Distributed Parallel Simulation of Primary Sample Space Metr...

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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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Simplifying the multi-GPU programming of a hyperspectral image registration algorithm

Simplifying the multi-GPU programming of a hyperspectral ima...

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2019 International conference on High Performance Computing and Simulation, HPCS 2019

作者： Fernandez-Fabeiro, Jorge Gonzalez-Escribano, Arturo Llanos, DIego R. Universidad de Valladolid Departamento de Informaticá Valladolid Spain

ISBN: (纸本)9781728144849

Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algorithms for this problem were not really devoted to real-time performance. The HYFMGPU algorithm arose as a high-performance GPU-based solution to solve such a lack. Nevertheless, a single-GPU solution is not enough, as sensors are evolving and then generating images with finer resolutions and wider wavelength ranges. An MPI+CUDA multi-GPU implementation of HYFMGPU was previously presented. However, this solution shows the programming complexity of combining MPI with an accelerator programming model. In this paper we present a new and more abstract programming approach for this type of applications, which provides a high efficiency while simplifying the programming of the multi-device parts of the code. The solution uses Hitmap, a library to ease the programming of parallel applications based on distributed arrays. It uses a more algorithm-oriented approach than MPI, including abstractions for the automatic partition and mapping of arrays at runtime with arbitrary granularity, as well as techniques to build flexible communication patterns that transparently adapt to the data partitions. We show how these abstractions apply to this application class. We present a comparison of development effort metrics between the original MPI implementation and the one based on Hitmap, with reductions of up to 95% for the Halstead score in specific work redistribution steps. We finally present experimental results showing that these abstractions are internally implemented in a high efficient way that can reduce the overall performance time in up to 37% comparing with the original MPI implementation. © 2019 IEEE.

关键词： Graphics processing unit

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30th International conference on Database and Expert Systems Applications, DEXA 2019

30th International Conference on Database and Expert Systems...

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30th International conference on Database and Expert Systems Applications, DEXA 2019

ISBN: (纸本)9783030276171

The proceedings contain 66 papers. The special focus in this conference is on Database and Expert Systems Applications. The topics include: Novel Node Importance Measures to Improve Keyword Search over RDF Graphs;Querying in a Workload-Aware Triplestore Based on NoSQL Databases;Reverse Partitioning for SPARQL Queries: Principles and Performance Analysis;PFed: Recommending Plausible Federated SPARQL Queries;representing and Reasoning About Precise and Imprecise Time Points and Intervals in Semantic Web: Dealing with Dates and Time Clocks;context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning;silverChunk: An Efficient In-Memory parallel Graph processing System;a Modular Approach for Efficient Simple Question Answering Over Knowledge Base;scalable Machine Learning in the R Language Using a Summarization Matrix;energy Efficient Data Placement and Buffer Management for Multiple Replication;ML-PipeDebugger: A Debugging Tool for Data processing Pipelines;correlation Set Discovery on Time-Series Data;anomaly Subsequence Detection with Dynamic Local Density for Time Series;trajectory Similarity Join for Spatial Temporal Database;multiviewpoint-Based Agglomerative Hierarchical Clustering;Triplet-CSSVM: Integrating Triplet-Sampling CNN and Cost-Sensitive Classification for Imbalanced image Detection;discovering Partial Periodic High Utility Itemsets in Temporal Databases;using Mandatory Concepts for Knowledge Discovery and Data Structuring;topological Data Analysis with ϵ -net Induced Lazy Witness Complex;analyzing Sequence Pattern Variants in Sequential Pattern Mining and Its Application to Electronic Medical Record Systems;querying Knowledge Graphs with Natural Languages;composing distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm;keyword Search Based Mashup Construction with Guaranteed Diversity;adaptive Caching for Data-Intensive Scientific Workflows in the Cloud;Succinct BWT-Based Sequence Prediction.

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A Compiler Framework for Fixed-topology Non-deterministic Finite Automata on SIMD Platforms 24

A Compiler Framework for Fixed-topology Non-deterministic Fi...

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24th IEEE International conference on parallel and distributed Systems (ICPADS)

作者： Nourian, Marziyeh Wu, Hancheng Becchi, Michela North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781538673089

Automata processing is at the core of many pattern matching applications. Previous efforts have proposed designs to accelerate automata traversal on various parallel platforms. Many existing acceleration methods store the finite automata states and transitions in memory. As a result, for these designs memory size and bandwidth are the main limiting factors to performance and power efficiency. Many applications, however, require processing several fixed-topology automata that differ only in the symbols associated to the transitions. This property enables the design of alternative, memory-efficient solutions. In this work, we target fixed-topology non-deterministic finite automata (NFAs) and propose a memory-efficient design that embeds the automata topology in code and stores only the transition symbols in memory. Our solution is suitable to SIMD architectures. In particular, we propose a compiler framework that, given a set of NFAs with fixed topology and the hardware configuration of the target SIMD platform, deploys the NFAs and the input streams to be processed onto the target device so as to exploit the available parallelism, maximize hardware utilization and optimize the memory access patterns. Our compiler framework performs a combination of platform agnostic and platform-specific design decisions and optimizations. We showcase our compiler framework on GPU, Intel Xeon Phi and Intel Xeon Skylake platforms.

关键词： Automata processing NFAs SIMD GPUs Intel Xeon Phi platforms

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