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Proceedings - 2020 5th international conference on Communication, Image and Signal processing, CCISP 2020

Proceedings - 2020 5th International Conference on Communica...

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5th international conference on Communication, Image and Signal processing, CCISP 2020

ISBN: (纸本)9781728185897

the proceedings contain 60 papers. the topics discussed include: an application recommendation method based on IUF;design of AES/EBU audio transceiver system based on FPGA;research on test platform of train control vehicle-mounted subsystem for high-speed railway;classification of the mask Augsburg speech corpus (MASC) using the consistency learning method;research on energy-saving collaborative optimization method for multiple trains considering renewable energy utilization;design of online open channel flow monitoring system based on LSPIV;a refined prior-box generator for anchor-based object detector;research on parallel system for motion states monitoring of the planetary rover;and surface damage detection method for blade of wind turbine based on image segmentation.

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Proceedings of the 11th Workshop on parallel Programming and Run-Time Management Techniques for Many-Core architectures / 9th Workshop on Design Tools and architectures for Multicore Embedded Computing Platforms, PARMA-DITAM 2020

Proceedings of the 11th Workshop on Parallel Programming and...

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11th Workshop on parallel Programming and Run-Time Management Techniques for Many-Core architectures / 9th Workshop on Design Tools and architectures for Multicore Embedded Computing Platforms, PARMA-DITAM 2020

the proceedings contain 5 papers. the topics discussed include: sparse matrix-dense matrix multiplication on heterogeneous CPU+FPGA embedded system;run-time power modelling in embedded GPUs with dynamic voltage and frequency scaling;fault-tolerant online scheduling algorithms for CubeSats;an OpenMP parallel genetic algorithm for design space exploration of heterogeneous multi-processor embedded systems;and automated precision tuning in activity classification systems: a case study.

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OpenCLTM Implementation of Rapid Image Restoration Kernels Based on Blind/Non-blind Deconvolution Techniques for Heterogeneous parallel Systems 5th

OpenCLTM Implementation of Rapid Image Restoration Kernels B...

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5th international conference on Microelectronics, Computing and Communication Systems, MCCS 2020

作者： Satapathy, Ashutosh Livingston, L. M. Jenila SCOPE Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9789811602740

Over the years, image enhancement is one of the research areas in the field of image processing and computer vision. In the real world, images or videos captured and transmitted by different sensors are not fit for further processing. Image restoration is the sub-branch of image enhancement used to reconstruct the original image from blur images using restoration models. It plays an immense role in many real-time applications such as traffic monitoring, vigilance system, human identification, and recognition, while it also requires faster implementation. In this paper, we have discussed different image degradation models and the most well-known restoration techniques such as Inverse, Pseudo Inverse filter, Wiener filter, Constraint Least Square Error filter, and Lucy Richardson filter are used to reestablish an original image from a distorted one. Apart from that, we have developed OpenCL kernels for those above filters for faster image reconstruction, which are also easily deployed on multiple heterogeneous parallel systems. At last, we have done a performance analysis of both OpenCL CPU and GPU implementation of the above restoration techniques in terms of accuracy and time consumption. Different performance metrics like Root Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, and Structural Similarity Index have used to evaluate the accuracy of the above techniques. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs 20th

Parallel SCC Detection Based on Reusing Warps and Coloring P...

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

作者： Hou, Junteng Wang, Shupeng Wu, Guangjun Ma, Bingnan Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Natl Comp Network Emergency Response Tech Team Coordinat Ctr China Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Detecting strongly connected components (SCCs) in directed graphs is crucial for analyzing the structure of graphs. It can accelerate the process of large-scale graphs with applying GPUs to graph computation. However, GPU-based SCC detection still faces the challenge from graphs, especially real-world graphs, skewing and strict synchronization requirement. In this paper, we proposed a new paradigm of identifying SCCs with reusing warps and coloring partitions. the scheme of reusing warps assigns multiple vertices to each virtual warp at one time, which greatly reduces the number of warps that are applied but assigned few tasks. Furthermore, we proved that the partitions formed by coloring independently contains all SCCs in them, so we use coloring to identify more partitions and parallel detect SCCs in each partition to get more SCCs. Finally, the colored partitions are deeply combined with the following traversal to optimize the algorithm. We conduct extensive theoretical and experimental analysis to demonstrate the efficiency and accuracy of our approach. the experimental results expose that our approach can achieve 7.23x, 30.55x, 1.75x and 1.26x speedup for SCC detection using NVIDIA K80 compared with algorithms of Tarjan, Barnat, Hong and Slota respectively.

关键词： Strongly connected components detection GPUs Reusing warps Coloring partitions Partitions combined with traversal

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Rise the Momentum: A Method for Reducing the Training Error on Multiple GPUs 19th

Rise the Momentum: A Method for Reducing the Training Error ...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Tang, Yu Yin, Lujia Zhang, Zhaoning Li, Dongsheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Deep neural network training is a common issue that is receiving increasing attention in recent years and basically performed on Stochastic Gradient Descent or its variants. Distributed training increases training speed significantly but causes precision loss at the mean time. Increasing batchsize can improve training parallelism in distributed training. However, if the batchsize is too large, it will bring difficulty to training process and introduce more training error. In this paper, we consider controlling the total batchsize and lowering batchsize on each GPU by increasing the number of GPUs in distributed training. We train Resnet50 [4] on CIFAR-10 dataset by different optimizers, such as SGD, Adam and NAG. the experimental results show that large batchsize speeds up convergence to some degree. However, if the batchsize of per GPU is too small, training process fails to converge. Large number of GPUs, which means a small batchsize on each GPU declines the training performance in distributed training. We tried several ways to reduce the training error on multiple GPUs. According to our results, increasing momentum is a well-behaved method in distributed training to improve training performance on condition of multiple GPUs of constant large batchsize.

关键词： Multiple GPUs Batchsize Distributed training Momentum

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Hybrid parallel Computation for Sparse Network Component Analysis 5th

Hybrid Parallel Computation for Sparse Network Component Ana...

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5th international conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Elsayad, Dina Hamad, Safwat Shedeed, Howida A. Tolba, M. F. Ain Shams Univ Fac Comp & Informat Sci Cairo Egypt

ISBN: (纸本)9783030311292;9783030311285

the gene regulatory network analysis primary goal is understanding the gene interactions topological order and how the genes influence each other. Network component analysis is a vital technique for build gene regulatory network. However, the network component analysis technique is time consuming and computational intensive. therefore, parallel techniques are required. PSparseNCA is a parallel network component algorithm. this work present an improved version of PSparseNCA, referred as hPSparseNCA (Hybrid parallel Computation for Sparse Network Component Analysis). hPSparseNCA uses the hybrid computational model to enhance the performance of PSparseNCA. hPSparseNCA is outperformed PSparseNCA achieving speedup reached 192.77 instead of 36.03 for PSparseNCA on 40 processing nodes. Furthermore, the speedup of the proposed algorithm reached 728.48 when running on 256 processing nodes.

关键词： Gene data Bioinformatics Component analysis High performance parallel Regulatory network

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processing in Memory Assisted MEC 3C Resource Allocation for Computation Offloading 20th

Processing in Memory Assisted MEC 3C Resource Allocation for...

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

作者： Yang, Yang Chang, Xiaolin Jia, Ziye Han, Zhu Han, Zhen Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China Xidian Univ Informat Sci Inst State Key Lab ISN Xian Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX USA

ISBN: (纸本)9783030602451;9783030602444

the improvement of Internet of things (IoT) applications has led to a substantial increase in the number of multiple resources of computation, communication, and caching (3C). the fifth generation (5G) and multi-access edge computing (MEC) are promising to enhance the computation offloading of IoT applications with high performance and reliability. According to resource-consuming preferences, IoT applications can be divided into computation-hungry applications and memory-hungry applications. To deal with the computation-hungry applications, Graphics processing Units (GPUs) are increasingly used to process simple computation tasks. Meanwhile, the running of memory-hungry applications is accompanied by massive data transfers between processing core and memory. these transfers can result in significant energy and performance costs. processing in memory (PIM) is a computing paradigm that avoids most data movement costs by performing a part of the computations directly in the memory. In this paper, we focus on offloading computation tasks in MEC that require 3C resources with high efficiency and low energy consumption considering latency and resilience constraints in a PIM-assisted multi-core (PAMC) architecture of physical machines (PMs). We formulate an optimization problem to minimize the total weighted resource costs and energy consumption. We also present an algorithm based on the column generation to solve the problem. Simulation results demonstrate that the proposed PAMC architecture can achieve good results in terms of energy consumption and resources utilization in comparison with the traditional PMs' architecture with the same resources.

关键词： Column generation Computation offloading GPU Multi-access edge computing processing in memory

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Proceedings of the 11th Workshop on Parallel Programming and...

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8th international Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2017

ISBN: (纸本)9781450375450

the proceedings contain 5 papers. the topics discussed include: sparse matrix-dense matrix multiplication on heterogeneous CPU+FPGA embedded system;run-time power modeling in embedded GPUs with dynamic voltage and frequency scaling;fault-tolerant online scheduling algorithms for CubeSats;an OpenMP parallel genetic algorithm for design space exploration of heterogeneous multi-processor embedded systems;and automated precision tuning in activity classification systems: a case study.

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An OpenMP parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems 2020

An OpenMP Parallel Genetic Algorithm for Design Space Explor...

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作者： Muttillo, Vittoriano Giammatteo, Paolo Fiorilli, Giuseppe Pomante, Luigi University of L’Aquila L’Aquila Italy

ISBN: (纸本)9781450375450

Heterogeneous multiprocessor platforms are becoming widespread in the embedded system domain, mainly for the opportunity to improve timing performance and to minimize energy/power consumption and costs. therefore, when using such platforms, it is important to adopt a Design Space Exploration (DSE) strategy that considers compromises among different objectives. Existing DSE approaches are generally based on evolutionary algorithms to solve Multi-Objective Optimization Problems (MOOPs) by minimizing a linear combination of weighted cost functions (i.e., Weighted Sum Method, WSM). In this way, the main issues are related to reduce timing execution while trying to improve the evolutionary algorithm performance, introducing strategies that attempt to bring better solutions. Code parallelization is one of the most used approaches in this field, but no standard methods have been released since different aspects could affect the performance. this approach leads to exploit parallel and distributed processing elements in order to implement evolutionary algorithms. In the latter case, if we consider genetic algorithms, it is possible to talk about parallel Genetic algorithms (PGA). Considering this context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an improvement that reduces execution time using parallel programming languages (i.e., OpenMP) inside the main genetic algorithm approach, while trying to lead to better partitioning solutions. the descriptions of the adopted DSE activities and the OpenMP implementation, validated by means of a case study, represent the core of the paper. © 2020 Association for Computing Machinery.

关键词： Multiobjective optimization

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Nahuatl Neural Machine Translation Using Attention Based architectures: A Comparative Analysis for RNNs and Transformers as a Mobile Application Service 20th

Nahuatl Neural Machine Translation Using Attention Based Arc...

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20th Mexican international conference on Artificial Intelligence (MICAI)

作者： Bello Garcia, Sergio Khalil Sanchez Lucero, Eduardo Bonilla Huerta, Edmundo Hernandez Hernandez, Jose Crispin Ramirez Cruz, Jose Federico Pedroza Mendez, Blanca Estela Tecnol Nacl Mexico Inst Tecnol Apizaco Tzompantepec 90300 Tlaxcala Mexico

ISBN: (纸本)9783030898205;9783030898199

Machine Translation is a problem that consists of automating the task of translating a sentence into another target language done by a computer, and is still in research, especially with low-resource languages. the neoteric introduction of attention techniques inside the Natural Language processing (NLP) field in coalescence with a broader disposal of word-segmentation and Web Scrapping techniques;including the lack of a proper online tool translation for Nahuatl dialect, inspired this work in an effort to produce such a tool. Once availability of suitable corpus via Web Scrapping is searched for with scrutiny, therefore, doubling the state of the art in parallel phrases;several vocabulary files were produced using two sorts of word segmentation tools in order to extract the morphemes and break down the agglutination Nahuatl contains. By performing a comparative analysis between Recurrent Neural Networks (RNNs) and Transformers, incorporating two segmentation techniques and two different corpus, it is possible to improve the state of the art regarding Nahuatl by more than four times the BLEU score (66.45) with second validation by using a Fuzzy similarity library. Such experiments confirmed the hypothesis that by increasing the corpus size by double, using transformers and sub-word segmentation, a translation from Spanish to Nahuatl is the best approach that can be accomplished so far with the current tools;outperforming many times Statistical Machine Translation (SMT) and RNNs which do not contain attention, plus the deployment of an application that serves as a platform for the language.

关键词： Neural Machine Translation Nahuatl Transformers Recurrent Neural Networks Mobile application

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