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X-Ray Computed Tomography Applied to Objects of Cultural Heritage: Porting and Testing the Filtered Back-Projection Reconstruction Algorithm on Low Power Systems-on-Chip

X-Ray Computed Tomography Applied to Objects of Cultural Her...

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Euromicro conference on parallel, Distributed and Network-Based processing

作者： Elena Corni Lucia Morganti Maria Pia Morigi Rosa Brancaccio Matteo Bettuzzi Giuseppe Levi Eva Peccenini Daniele Cesini Andrea Ferraro Department of Physics and Astronomy University of Bologna Bologna Italy Bologna Section Italian Institute for Nuclear Physics (INFN) Bologna Italy Enrico Fermi Center for Study and Research Roma Italy CNAF Section Italian Institute for Nuclear Physics (INFN) Bologna Italy

the embedded and high-performance computing (HPC) sectors, that in the past were completely separated, are now somehow converging under the pressure of two driving forces: the release of less power consuming server processors and the increased performance of the new low power Systems-on-Chip (SoCs) developed to meet the requirements of the demanding mobile market. this convergence allows the porting to low power embedded architectures of applications that were originally confined to traditional HPC systems. In this paper, we present our experience of porting the Filtered Back-projection Algorithm to a low power, low cost system-on-chip, the NVIDIA Tegra K1, which is based on a quad core ARM CPU and on a NVIDIA Kepler GPU. this Filtered Back-projection Algorithm is heavily used in 3D Tomography reconstruction software. the porting has been done exploiting various programming languages (i.e. OpenMP, CUDA) and multiple versions of the application have been developed to exploit both the SoC CPU and GPU. the performances have been measured in terms of 2D slices (of a 3D volume) reconstructed per time unit and per energy unit. the results obtained with all the developed versions are reported and compared with those obtained on a typical x86 HPC node accelerated with a recent NVIDIA GPU. the best performances are achieved combining the OpenMP version and the CUDA version of the algorithm. In particular, we discovered that only three Jetson TK1 boards, equipped with Giga Ethernet interconnections, allow to reconstruct as many images per time unit as a traditional server, using one order of magnitude less energy. the results of this work can be applied for instance to the construction of an energy-efficient computing system of a portable tomographic apparatus.

关键词： Graphics processing units Image reconstruction Computer architecture Filtering algorithms Computed tomography Radiography

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Low-Latency High-throughput Decoder of QC-LDPC Codes by Matrix Splitting

Low-Latency High-Throughput Decoder of QC-LDPC Codes by Matr...

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international conference on Wireless Communications and Signal processing (WCSP)

作者： Xinyu Zhong Shuaicong Gong Ming Jiang Chunming Zhao Southeast University

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

As the continuously increasing demand of high data rate and lower transmission, many modern communication systems employ the LDPC coding scheme with ultra-high throughput decoder to achieve these challenging requirements. However, due to the implementation complexity of decoding algorithm, designing an ultra-high throughput decoder of LDPC codes still suffers many difficulties. therefore, we propose a FPGA-based LDPC decoder with partial parallel decoding, which splits and virtually supplements the parity-check matrix to achieve higher throughput and lower latency. Our proposed FPGA-based decoder can achieve a throughput of 6.072Gbps in 10 iterations for the highest rate 5G-LDPC code.

关键词： Wireless communication Signal processing algorithms Signal processing throughput Decoding Timing Table lookup Low latency communication Iterative decoding Field programmable gate arrays

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Design and Implementation of Driverless Perceptual System Based on CPU + FPGA

Design and Implementation of Driverless Perceptual System Ba...

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international conference on Cybernetics, Robotics and Control (CRC)

作者： Zhifang Yang Lei Yan Jiakai Yuan School of Electrical Information Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781728186245

ISBN: (纸本)9781728186252

Nowadays, autonomous driving is one of the most popular technologies and is in a stage of rapid development. In the process of working, autonomous vehicles need to locate, perceive, predict and plan. the enormous amount of calculation results high energy consumption. In response to the characteristics of unmanned driving, a high performance, low power consumption and high flexibility unmanned visual perception system was developed using a heterogeneous platform of CPU+FPGA. the heterogeneous platform has the advantages of parallel processing and field programmable, etc. the system can improve the YOLOv3(You Only Look Once) algorithm of the deep neural network for target detection recognition, the Soft-NMS (Non maximum suppression) algorithm is added to the original YOLOv3 algorithm framework, and then the target detection false detection rate and missed detection rate are minimized through the frame regression operation, and finally we train data and test the network. the experimental results show that the system can quickly and accurately identify the target in different complex scenes, and the power consumption is low.

关键词： Power demand Target recognition Object detection parallel processing Autonomous vehicles Robots Visual perception

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A Scheduling Method for Avoiding Kernel Lock thrashing on Multi-cores

A Scheduling Method for Avoiding Kernel Lock Thrashing on Mu...

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international conference on parallel and Distributed Systems (ICPADS)

作者： Yan Cui Weida Zhang Yu Chen Yuanchun Shi Department of Computer Science and Technology Tsinghua University China

Multi-core architectures have been adopted in various computing environments. Predictions based on Moore's Law state that thousands of cores can be integrated on a single chip within 10 years. To achieve better performance and scalability on multi-cores, applications should be multi-threaded, and therefore threads assigned on different cores can execute concurrently. However, lock contention in kernels can affect the scalability so significantly that the speedup decreases with the increasing number of cores (thrashing). Existing efforts to address this problem mainly focus on deferring lock thrashing, and therefore these techniques cannot prevent thrashing fundamentally. In this paper, we propose to use lock-aware scheduling to avoid thrashing. Our method detects thrashing on a per-thread basis and migrates contended threads to a smaller set of cores. the optimal number of cores is determined by maximizing the proposed normalized throughput model of migrated threads. the proposed method is implemented in Linux 2.6.29.4 and evaluated on a 32-core system. Experimental results on a series of lock-intensive micro- and macro-benchmarks show the effectiveness: for 3 of 5 workloads exhibiting thrashing behaviour, lock-aware scheduling can detect the speedup decrease accurately and sustain the maximal speedup, for the remaining 2 workloads, the performance can be improved greatly although the maximal speedup is not sustained, for 1 workload which does not suffer thrashing, the method introduces negligible runtime overhead.

关键词： Multicore processing Kernel Instruction sets Linux Benchmark testing throughput Complexity theory

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Power Efficient Motion Estimation using Multiple Imprecise Metric Computations

Power Efficient Motion Estimation using Multiple Imprecise M...

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IEEE international conference on Multimedia and Expo (ICME)

作者： In Suk Chong Antonio Ortega Signal and Image Processing Institute University of Southern California Los Angeles CA USA

In this paper, we propose power efficient motion estimation (ME) using multiple imprecise sum of absolute difference (SAD) metric computations. We extend the recent work of Varatkar et al (2006) by providing analytical solutions based on modelling of computation errors due to voltage overscaling (VOS) and sub-sampling (SS). Results show that our solutions provide significantly better performance in the sense of rate increase for fixed QP, e.g., less than 5% increase, while in Varatkar et al (2006) the rate increase could be as high as 20%. It allows us to apply lower voltage which leads to additional power saving. Our analysis also allows us to compare different ME algorithms (e.g., full search vs. a fast algorithm) and SAD computation architectures (parallel vs. serial) in terms of their robustness to imprecise metric computations and their power efficiency. Finally, we demonstrate that additional power savings can be achieved by removing redundancy between the various computations.

关键词： Motion estimation Voltage Error correction Analytical models Computational modeling Algorithm design and analysis Concurrent computing Computer architecture Redundancy MPEG 4 Standard

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Design of Automatic Colouring of Sketch Images Based on Deep Learning Algorithm

Design of Automatic Colouring of Sketch Images Based on Deep...

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Information Science, parallel and Distributed Systems (ISPDS), international conference on

作者： Lian Chen Chengjun Zhou School of Art and Design Hubei University of Technology China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In this paper, we have designed an innovative deep learning model using U-net with PatchGAN discriminator aiming at automatic colouring of sketch images, which provides a novel solution for sketch colouring. Firstly, the image is passed through the dataset to the generator, which receives the input image and tries to convert it into the expected output image. Subsequently, in each iteration, the loss functions of the generator and the discriminator are calculated and the model parameters are updated by the optimiser. Finally, predictions are made on the test dataset and the results are plotted. By passing new unseen data to the trained generator, it was observed that the generation results were as expected and the deep learning coloured images were very close to the actual coloured images. this research brings important implications for the fields of digital art and computer vision. Firstly, it provides a completely new approach to processing sketch images, opening up more possibilities for artists and designers. Second, the model expands new ideas and examples for the application of deep learning techniques in the field of image processing. Finally, in practical applications, this auto-colouring technique is expected to greatly improve productivity and can be widely used in animation, comics and other fields.

关键词： Deep learning Productivity Information science Computer vision Image processing Computational modeling Digital art

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Implementing Hadoop and Moodle Integrated Student Support System

Implementing Hadoop and Moodle Integrated Student Support Sy...

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international conference on Advanced Information Technologies (ICAIT)

作者： Myint Myat Aung Shin Min Htet Hein Myo Set Aung Hpone thant Sithu Htut Kyaw Zin Htet Nwe Nwe Myint thein Data Distribution Management Lab University of Information Technology Yangon Myanmar Data Distribution Management Lab Faculty of Computer Science University of Information Technology Yangon Myanmar

ISBN: (数字)9798331510046

ISBN: (纸本)9798331510053

Learning management systems (LMS) generate large amounts of real-time data which has the potential to provide valuable insights. Hadoop MapReduce is a scalable framework that is designed to handle such data. this paper presents a system architecture that integrates Hadoop MapReduce into Moodle, a popular LMS, for analyzing academic data. the implementation of the architecture allows users to generate a variety of reports from student attendance logs through a Moodle plugin which utilizes the Hadoop cluster for processing data. Users such as lecturers can then utilize these reports to identify which students do and do not engage in which courses as well as monitor their eligibility for exams. the use of Hadoop MapReduce provides high performance computing (HPC) capabilities that offer distributed parallel processing and economical scalability by means of adding commodity nodes to the cluster.

关键词： Learning management systems Scalability Systems architecture Computer architecture parallel processing Market research Real-time systems Synchronization Information technology Monitoring

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Multilingual Sentiment Analysis using Deep-Learning architectures

Multilingual Sentiment Analysis using Deep-Learning Architec...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Praveen Dominic Niranjan Purushothaman Anish Skanda Anil Kumar A Prabagaran J Angelin Blessy A John School of Graduate Studies MSc in Big Data Analytics Trent University Ontario Canada Visa Consolidated Support Services India Pvt. Ltd Bengaluru India School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. the posts and tweets shared by the people on social media can be mined to infer the valuable ‘mindset’ of the people which is much required for any ruling government in the world. the objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. the idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. the pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). the larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. the non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries’ sentiment and their corresponding reliance on Russian oil is performed.

关键词： Sentiment analysis Analytical models Social networking (online) Oils Government Bit error rate Machine learning

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A Framework for exploration of parallel SystemC simulation on the single-chip cloud computer 12

A Framework for exploration of parallel SystemC simulation o...

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Proceedings of the 5th international ICST conference on Simulation Tools and Techniques

作者： Christoph Roth Simon Reder Oliver Sander Michael Hübner Jürgen Becker Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (纸本)9781450315104

Since the number of cores integrated on a single die is expected to increase steadily, new memory and communication architectures as well as programming methods are needed and currently explored by manufacturers. In this context, Intel Labs developed the Single-chip Cloud Computer (SCC), a 48-core experimental processor, serving as a platform for many-core software research. Within this paper a framework targeting the investigation of SystemC kernel parallelization on the SCC is presented. the framework provides the basis for implementation of different synchronization schemes while combining distributed and shared memory programming models and exploiting multiple distinct address spaces. As a case study, a synchronous parallelization scheme is preliminarily evaluated by means of several simulation models of different accuracy. Results of the analysis give a first evidence of the applicability of the synchronous parallelization method on the homogeneous non-cache coherent manycore architecture of the SCC for detailed system simulation.

关键词： single-chip cloud computer parallel simulation SystemC

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Cluster analysis for the cloud: parallel competitive fitness and parallel K-means++ for large dataset analysis

Cluster analysis for the cloud: Parallel competitive fitness...

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IEEE international conference on Cloud Computing Technology and Science (CloudCom)

作者： Rui Maximo Esteves thomas Hacker Chunming Rong Department of Electrical and Computer Engineering University of Stavanger Norway Department of Computer and Information Technology Purdue University West Lafayette USA

the amount of resources needed to provision Virtual Machines (VM) in a cloud computing systems to support virtual HPC clusters can be predicted from the analysis of historic use data. In previous work, Hacker et al. found that cluster analysis is a useful tool to understand the underlying spatio-temporal dependencies present in system fault and use logs. However, the cluster analysis used for reducing spatio-temporal dependences should be fast and accurate to understand the underlying stochastic properties of these systems. K-means is a fast cluster analysis method, in which accuracy depends on the use of initialization algorithms that are usually serial and slow. In this paper we present two new parallel strategies for fast seeding K-means cluster analysis. Both strategies were tested on a real problem where the aim was to reduce spatial and temporal dependencies of failures on large supercomputer systems. the performance of both strategies were compared with five existing serial implementations: K-means implementations of 1) Lloyd (L); 2) McQueen (M); and 3) Hartigan - Wong (HW), all of them using Forgy seeding; 4) K-means++; and 5) Neural Gas clustering (NG), a more recent and sophisticated method. Our results show that our new parallel Competitive Fitness approach reduces the Within Sum of Squares (WSQQ) measure, thus increasing cluster quality of the three K-means implementations: L; M; HW, and is 200 times faster than the existing serial K-means++. the existing serial and our new parallel K-means++ have the lowest WSQQ. Our new parallel K-means++ is twice as fast as the existing serial K-means++ method, and is 4 times faster than the NG method. Moreover, our new methods did not generate empty clusters, while NG did. As a result of our new techniques, predicting the amount of resources needed to provision VMs processing historic system fault and use data can now be done faster and with more accuracy.

关键词： Clustering algorithms Algorithm design and analysis Partitioning algorithms Cloud computing conferences Computer hacking Accuracy

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