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OpenCLTM Implementation of Fast Frequency Domain Image Denoisification Kernels for parallel Environments 5th

OpenCLTM Implementation of Fast Frequency Domain Image Denoi...

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5th international conference on Nanoelectronics, Circuits and Communication Systems, NCCS 2019

作者： Satapathy, Ashutosh Jenila Livingston, L.M. School of Computing Science and Engineering Vellore Institute of Technology Vellore Chennai600127 India

ISBN: (纸本)9789811574856

From the last couple of decades, image denoisification is one of the challenging areas in the image processing and computer vision domains that adds clarity to images by removing noise and makes them suitable for further processing. Images or videos captured by various visual sensors are degenerated during various circumstances such as sensor temperature, light intensity, target environment, erroneous electric instruments, and interference in the communication channels and required to be enhanced by various filtering techniques. Most of the real-world applications like student monitoring, vigilance system, traffic analysis, target detection, and classification use image smoothing to reduce artifacts present in the captured images and additionally demand its quicker execution. In this paper, we have explained different types of noise models normally degrade the quality of an image and the three most popular frequency domain smoothing filters (ideal, Gaussian, and Butterworth) are available to remove those noises. In addition to that, OpenCL kernels of those filters have been implemented for rapid denoisification and can be easily portable on wide verities of parallel devices. At last, performance evaluation has been carried out for both the CPU and GPU implementations to seek out their productiveness on those noise models in terms of time and precision. Various performance assessment metrics like entropy, root-mean-squared error, peak signal-to-noise ratio, and mean absolute error are applied to determine the rightness of the above filters. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Low pass filters

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On GPU optimizations of stencil codes for highly parallel simulations

On GPU optimizations of stencil codes for highly parallel si...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Nikolai Pfisterer Marco Berghoff Achim Streit Steinbuch Centre for Computing Karlsruhe Institute of Technology Karlsruhe Germany

Stencil codes are valuable methods to solve partial differential equations of models in a wide range of applications in science and engineering. Graphics processing units (GPUs) provide a highly parallel architecture with fast directly accessible memory that is desirable to run stencil codes. They enable larger and more complex simulations that are solved faster compared to simulating on CPUs. We provide a solution for users to run highly parallel stencil codes on GPUs, that can also be efficiently used on a distributed GPU *** this work, we present an extension to the multi-disciplinary framework NAStJA originally designed to efficiently run stencil codes on the CPUs in current high-performance computing (HPC) systems, to also be able to run on GPUs. We describe different methods to increase the performance of stencil codes on GPUs like a border exchange method which is transparent to the user, as well as a buffer for gradient values that are needed multiple times. We show their performance using the phase-field method as a stencil code *** this GPU extension and optimizations implemented into the NAStJA framework, we can show highly improved performance compared to the CPU implementation, and an efficiency of 92% (weak scaling) for a large-scale example simulation on 64 GPUs on the ForHLR II HPC system.

关键词： Partial differential equations Graphics processing units Clustering algorithms Performance gain parallel architectures Central processing Unit Mathematical model

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distributed Algorithm for Truss Maintenance in Dynamic Graphs 1

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21st international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2020

作者： Luo, Qi Yu, Dongxiao Sheng, Hao Yu, Jiguo Cheng, Xiuzhen School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Qilu University of Technology Jinan China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

Cohesive subgraphs are applied in various fields. Mining cohesive components such as k-truss have attracted a lot of effort to improve time efficiency in large-scale graphs. The k-truss is a subgraph where each edge is contained in at least k- 2 triangles and the problem of truss decomposition is computing the k-trusses of a graph for all k. However, most graphs in real scenarios are usually changing over time. The previous studies take the static graphs as input, and the truss maintenance in dynamic graphs receives little attention. This paper focuses on distributed algorithms for truss maintenance. We present a distributed model underlying the real distributed processing model Pregel. Based on the model, we propose truss decomposition and truss maintenance algorithms. To confirm the effectiveness and efficiency of the proposed algorithms, we conduct extensive experiments over both real-world and synthetic graphs. © 2021, Springer Nature Switzerland AG.

关键词： Maintenance

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Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters 26

Communication-Aware Load Balancing of the LU Factorization o...

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Nesi, Lucas Leandro Schnorr, Lucas Mello Legrand, Arnaud PPGC UFRGS Inst Informat Porto Alegre RS Brazil Univ Grenoble Alpes LIG Grenoble INP INRIACNRS F-38000 Grenoble France

ISBN: (纸本)9781728190747

Supercomputers are designed to be as homogeneous as possible but it is common that a few nodes exhibit variable performance capabilities due to processor manufacturing. It is also common to find partitions equipped with different types of accelerators. Data distribution over heterogeneous nodes is very challenging but essential to exploit all resources efficiently. In this article, we build upon task-based runtimes' flexibility of managing data to study the interplay between static communication-aware data distribution strategies and dynamic scheduling of the linear algebra LU factorization over heterogeneous sets of hybrid nodes. We propose two techniques derived from the state-of-the-art 1D x 1D data distributions. First, to use fewer computing nodes towards the end to better match performance bounds and save computing power. Second, to carefully move a few blocks between nodes to optimize even further the load balancing among nodes. We also demonstrate how 1D x 1D data distributions, tailored for heterogeneous nodes, can scale better with homogeneous clusters than classical block-cyclic distributions. Validation is carried out both in real and in simulated environments under homogeneous and heterogeneous platforms, demonstrating compelling performance improvements.

关键词： Data Partitioning LU Factorization Load Balancing Task-Based applications Heterogeneous Clusters

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Intelligent model for lung cancer detection using fusion analysis in CT images

Intelligent model for lung cancer detection using fusion ana...

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The 3rd international conference on distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： F. Taher A. Abdelaziz College of Technological Innovation Zayed University Dubai UAE Nova Information Management School Universidade Nova de Lisboa 1070-312 Lisboa Portugal

One of the leading causes of death worldwide, lung cancer is the habitual use of tobacco products. Human life is threatened by unchecked cell development in the lung tissue. The use of CAD methods, namely correct demonstration, allows for early detection of lung cancer. An enhanced kind of artificial neural network, deep learning (DL) techniques use multiple layers to process their input, produce high-order characteristics, and finally output a projected value. The convolutional neural network (CNN) is one kind of deep learning that has found extensive use in computer vision applications. Rapid and accurate illness diagnosis became more challenging for radiologists as the volume of CT scan pictures increased. Hence, DL approaches were applied for the early identification of lung cancer as the need for CAD for lung cancer diagnosis increased. To train and test CAD, we examined 15,000 images of histopathology slides of adenocarcinomas, lung squamous cell carcinoma, and three kinds of benign lung tissue that were made accessible to the public. Extracting features from images required the use of multi-scale processing. The research was completed by comparing seven different CNN models for lung cancer classification: To ensure optimal performance, all pre-trained models are giggly to optimize parameters. With this approach, you can count on a minimal classification error of 0.090 and overall accuracy of 98.67 percent.

关键词： CAD cancer computer vision computerised tomography feature extraction image classification lung medical image processing tumours

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Improvement of searching methodology for network applications using bloom filters and hashing 6

Improvement of searching methodology for network application...

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6th international conference on parallel, distributed and Grid Computing, PDGC 2020

作者： Pandey, Dhiraj Pandey, Kavita JSS Academy of Technical Education Noida India Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781728171326

To reduce processing and networking costs, probabilistic techniques are very much popular for many network solutions. Binary Search Trees provide searching with worst-case time complexity of O (log n). In this work, we propose a generic novel way in which Bloom Filters are used with Hashing to reduce the search time complexity. The proposed approach provides a solution using a Hash Table in Bloom Filter. The objective is to reduce the worst-case complexity of hashing to O (1) and provide space and time complexity of O(1) at the cost of introducing a controlled number of False Positives and to guarantee no occurrence of False Negatives. The proposed approach provides a solution to this problem by using the hash tables in Bloom Filter. Two types of test cases have been used in this work for testing purposes in the scenario of Hashing to show the problem with Hashing and in the approach with Bloom Filters showing the solution to the problem that exists in Hashing. Results indicate that Bloom Filter-based approach can be used to obtain optimal performance in time elapsed in searching. © 2020 IEEE.

关键词： Cost reduction

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Building representative and balanced datasets of OpenMP parallel regions

Building representative and balanced datasets of OpenMP para...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Jordi Alcaraz Steven Sleder Ali TehraniJamsaz Anna Sikora Ali Jannesari Joan Sorribes Eduardo Cesar Universitat Autònoma de Barcelona Cerdanyola Spain Iowa State University Ames IA USA

ISBN: (纸本)9781665414555;9781665447645

Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced and representative datasets of parallel applications' executions. This work proposes a methodology for building datasets of OpenMP parallel code regions patterns. It allows for determining whether a given code region covers a unique part of the pattern input space not covered by the patterns already included in the dataset. The proposed methodology uses hardware performance counters to represent the execution of the region, which is referred to as the region signature for a given number of cores. Then, a complete representation of the region is built by joining the signatures for every different thread configuration in the system. Next, correlation analysis is performed between this representation and the representation of all the patterns already in the training set. Finally, if this correlation is below a given threshold, the region is considered to cover a unique part of the pattern input space and is subsequently added to the dataset. For validating this methodology, an example dataset, obtained from well known benchmarks, has been used to train a carefully designed neural network model to demonstrate that it is able to classify different patterns of OpenMP parallel regions.

关键词： Training Correlation Buildings Tools Hardware Performance analysis Tuning

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2020 3rd international conference on Engineering Technology and its applications, IICETA 2020

2020 3rd International Conference on Engineering Technology ...

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3rd international conference on Engineering Technology and its applications, IICETA 2020

ISBN: (纸本)9781728182315

The proceedings contain 47 papers. The topics discussed include: design a tracing system for a seed supply chain based on blockchain;new S-box transformation Based on chaotic system for image encryption;a cost-efficient management protocol for mobile crowd-sensing in urban vehicular scenarios;transmission mode selection for reliable critical data transmission;Iraqi e-learning adoption trials for building sustained developed society;performance measurement of processes and threads controlling, tracking and monitoring based on shared-memory parallel processing approach;galactic swarm optimization based adaptive digital image watermarking for optimal positions detection;a proposed image compression technique based on DWT and predictive techniques;security enhancement of AES-CBC and its performance evaluation using the avalanche effect;and distributed transform encoder to improve Diffie-Hellman protocol for big message security.

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Efficient Label-Constrained Shortest Path Queries on Road Networks: A Tree Decomposition Approach 48th

Efficient Label-Constrained Shortest Path Queries on Road Ne...

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48th international conference on Very Large Data Bases, VLDB 2022

作者： Zhang, Unhua Yuan, Long Li, Wentao Qin, Lu Zhang, Ying Nanjing University of Science and Technology China University of Technology Sydney Australia

Computing the shortest path between two vertices is a fundamental problem in road networks. Most of the existing works assume that the edges in the road networks have no labels, but in many real applications, the edges have labels and label constraints may be placed on the edges appearing on a valid shortest path. Hence, we study the label-constrained shortest path queries in this paper. In order to process such queries efficiently, we adopt an index-based approach and propose a novel index structure, LSD-Index, based on tree decomposition. With LSD-Index, we design an efficient query processing algorithm with good performance guarantees. Moreover, we also propose an algorithm to construct LSD-Index and further improve the efficiency of index construction by exploiting the parallel computing techniques. We conduct extensive performance studies using large real road networks including the whole USA road network. Compared with the state-of-the-art approach, the experimental results demonstrate that our algorithm not only achieves up to 2 orders of magnitude speedup in query processing time but also consumes much less index space. Meanwhile, the indexing time is also competitive, especially that for the parallel index construction algorithm. © 2021, VLDB Endowment. All rights reserved.

关键词： Query processing

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distributed-Memory Sparse Kernels for Machine Learning

Distributed-Memory Sparse Kernels for Machine Learning

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international Symposium on parallel and distributed processing (IPDPS)

作者： Vivek Bharadwaj Aydı n Buluç James Demmel EECS Department University of California Berkeley USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley USA

ISBN: (数字)9781665481069

ISBN: (纸本)9781665481076

Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times Dense Matrix Multiplication (SpMM) appear in diverse settings, such as collaborative filtering, document clustering, and graph embedding. Frequently, the SDDMM output becomes the input sparse matrix for a sub-sequent SpMM operation. Existing work has focused on shared memory parallelization of these primitives. While there has been extensive analysis of communication-minimizing distributed 1.5D algorithms for SpMM, no such analysis exists for SDDMM or the back-to-back sequence of SDDMM and SpMM, termed FusedMM. We show that distributed memory 1.5D and 2.5D algorithms for SpMM can be converted to algorithms for SD-DMM with identical communication costs and input / output data layouts. Further, we give two communication-eliding strategies to reduce costs further for FusedMM kernels: either reusing the replication of an input dense matrix for the SDDMM and SpMM in sequence, or fusing the local SDDMM and SpMM kernels. We benchmark FusedMM algorithms on Cori, a Cray XC40 at LBNL, using Erdos-Renyi random matrices and large real-world sparse matrices. On 256 nodes with 68 cores each, 1.5D FusedMM algorithms using either communication eliding approach can save at least 30% of time spent exclusively in communication compared to executing a distributed-memory SpMM and SDDMM kernel in sequence. Our 2.5D communication-eliding algorithms save 21 % of communication time compared to the unoptimized sequence. On real-world matrices with hundreds of millions of edges, all of our algorithms exhibit at least a 10x speedup over the SpMM algorithm in PETSc. On these matrices, our communication-eliding techniques exhibit runtimes up to 1.6 times faster than an unoptimized sequence of SDDMM and SpMM. We embed and test the scaling of our algorithms in real-world applications, including collaborative filtering via alternating-least-squares and inference for attention-based graph neural networks.

关键词： Machine learning algorithms Costs Runtime Collaborative filtering Layout Machine learning Inference algorithms

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