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A GPU enhanced LIDAR Perception System for Autonomous Vehicles

A GPU enhanced LIDAR Perception System for Autonomous Vehicl...

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euromicro conference on parallel, distributed and network-based processing

作者： Abderrahim Haneche Yazid Lachachi Smail Niar Hamza Ouarnoughi Ecole Nationale Supérieure d’Informatique Algiers Algeria Université des sciences et de la technologie d’Oran Oran Algeria LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Environment vision and understanding is a crucial task in Autonomous Driving (AD) context. This mainly needs image processing approaches such as Convolutional Neural networks (CNN). Nevertheless, cameras have shown their limits for such a task, especially in dealing with difficult light conditions. LIDAR is a powerful and widely used sensor for AD. Indeed, LIDAR can then cope with the lack of information gathered from cameras. For AD, data processing from the sensors is the key function to obtain a high quality perception. For this, Graphics processing Unit (GPU) platforms show great performances and outperform other processing platforms such as FPGA and Multi-cores. This work presents a new approach to produce multiple 2D representation from 3D points cloud coming from LIDAR. The 2D representation can therefore be used by any efficient image processing applications. Our approach uses only LIDAR sensor and exploits the high GPU parallelism for its implementation. The resulting 2D representations are then used by CNN for AD applications such as image classification and segmentation. Finally, our contributions have been evaluated using the KITTI road benchmark and showed encouraging results.

关键词： Laser radar Graphics processing units Two dimensional displays Three-dimensional displays Task analysis Cameras Rendering (computer graphics)

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Effective Anomaly Detection based on Reinforcement Learning in network Traffic Data

Effective Anomaly Detection Based on Reinforcement Learning ...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Zhongyang Wang Yijie Wang Hongzuo Xu Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo

关键词： Knowledge engineering Machine learning algorithms conferences distributed databases Reinforcement learning Telecommunication traffic network security

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Satellite network Traffic Scheduling Algorithm based on Multi-Agent Reinforcement Learning

Satellite Network Traffic Scheduling Algorithm Based on Mult...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Tingting Zhang Mingqi Zhang Lintao Yang Tao Dong Jie Yin Zhihui Liu Jing Wu Hao Jiang State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information Engineering Beijing China Electronic Information School Wuhan University Beijing China College of Physical Science and Technology Central China Normal University Wuhan China Electronic Information School Wuhan University Wuhan China

Traditional network traffic scheduling algorithms are difficult to accurately analyze and model. In the face of satellite networks with complex network structures, diverse transmission resources, and high dynamics, routing strategies lack flexibility. In order to reduce the possibility of network congestion in the multi-layer satellite network, a satellite network traffic scheduling algorithm based on multi-agent reinforcement learning is designed. By improving the experience pool and combining the priority sampling mechanism, the training speed and generalization ability of the model are improved. By comparing with the other four classic algorithms, we verified that this algorithm has a faster convergence speed and a better flow balance effect.

关键词： Training Satellites Scheduling algorithms Telecommunication traffic Reinforcement learning Complex networks Routing

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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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Off-Chip Congestion Management for GPU-based Non-Uniform processing-in-Memory networks

Off-Chip Congestion Management for GPU-based Non-Uniform Pro...

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euromicro conference on parallel, distributed and network-based processing

作者： Kishore Punniyamurthy Andreas Gerstlauer The University of Texas at Austin

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Advancements in packaging and 3D-stacking technology have enabled novel non-uniform processing-in-memory (NUPIM) architectures integrating a network of hybrid modules each with varying sizes and proportions of compute and memory. In such systems, network congestion can have a significant impact on performance and energy-efficiency scaling. At the same time, NUPIM systems often have strict power and thermal constraints, where traditional routing/redundancy or buffering based approaches to reduce congestion cannot be *** this paper, we propose a low-overhead yet scalable scheme for congestion management in off-chip networks of emerging GPU-based NUPIM systems. We note that congestion in NUPIM networks exhibits a form of tree saturation in which individually congested links form congested paths, where congestion effects due to back pressure vary across paths. Additionally, memory responses can have different performance impact due to memory divergence. Our approach exploits differences in performance impact of NUPIM traffic to mitigate congestion effects. We dynamically track congested paths and divergent data to manage priorities and accelerate performance-critical traffic. We further use critical paths knowledge to dynamically manage link widths and save I/O *** show that our congestion management outperforms a configuration with double the number of virtual channels and buffers indicating its effectiveness in managing congestion under stringent constraints. It improves performance by 15% (and up to 31%) over baseline and 10% (and up to 29%) against a memory distance based approach for congestion-affected benchmarks. Our scheme reduces 78% of I/O energy on average and up to 29% of system energy across all benchmarks.

关键词： Benchmark testing Delays Market research Systems architecture Packaging Memory management

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Acceleration of Radiofrequency Ablation Process for Liver Cancer Using GPU

Acceleration of Radiofrequency Ablation Process for Liver Ca...

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euromicro conference on parallel, distributed and network-based processing

作者： Claudio Schepke Marcelo Cogo Miletto Laboratório de Estudos Avançados (LEA) Federal University of Pampa - UNIPAMPA Campus Alegrete Brazil Grupo de Processamento Paralelo e Distribuído - GPPD Federal University of Rio Grande do Sul - UFRGS Campus do Vale Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Computational simulation is a technique used in several research areas. In medicine, the Radiofrequency Ablation Finite Element Method (RAFEM) application was developed to simulate the RadioFrequency Ablation (RFA) process, which is a medical procedure to treat hepatic cancer. This application presents a high computational time to perform a simulation, taking up to 20 hours for simulation while the RFA procedure itself lasts from four to six minutes. Some efforts have already been carried out to obtain better performance for the application. However, none of them considered the use of GPUs, which is an architecture that can be used to accelerate finite element method applications. This work aims to propose a parallelization approach to explore ways to obtain better performance for the application through the use of GPUs to reduce the execution time under a few minutes. We adopted an iterative development process to coordinate and to generate parallel versions of the code. Through this process allied to the use of GPU-accelerated code libraries, it was possible to create different versions of the application, adding improvements to each one. The results obtained with the best version showed a reduction of the computation time by up to 18 times. Therefore, this improvement makes the use of RAFEM application more feasible in health treatment by reducing the waiting time for the results.

关键词： Finite element analysis Mathematical model Graphics processing units Liver Computational modeling Matrices Acceleration

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Edge-based Sampling for Complex network with Self-Similar Structure

Edge-Based Sampling for Complex Network with Self-Similar St...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Wenting Wang Xianghua Fu Xinze Lin Shenzhen Technology University Shenzhen China

The network with self-similarity is an essential data structure widely used in social networks and bioinformatics. The network sampling as an important method for subgraph mining has attracted attention in many applications. However, traditional methods are computationally complexity and time- consuming. Due to the increase of the data scale, the algorithms’ runtime grows exponentially. A new methodology needs to handle the computational complexity challenge from large-scale graph datasets. In this paper, we develop a series of edge-based sampling algorithms: HEPS-1 and HEPS-2. We investigate the relations between edges and their edge neighbours caused by the self- similarity. Then, we determine the edges for the subgraph. In this way, we sample the networks while keeping the original statistical characters. The computational complexity is low. The effectiveness of sampling is enhanced.

关键词： Runtime Social networking (online) Complex networks Data structures Data mining Computational complexity Bioinformatics

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Artificial Accurate Time-stamp in network Adapters*

Artificial Accurate Time-stamp in Network Adapters*

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Wojciech Waśko Dotan David Levi Teferet Geula Amit Mandelbaum Nvidia Młynek Poland Nvidia Yokne’am Israel Nvidia Jerusalem Israel

Nanosecond-level synchronization of network nodes is an enabler for a wide range of distributed applications. The achievable accuracy of network synchronization is bounded by measurement errors in both packet ingress and egress timestamps provided by the network adapters. Today’s commodity network adapters provide time-stamps with an accuracy of, at best, 100s of nanoseconds. Improving the accuracy rate of the time-stamp would require a new hardware design and take years to deploy. In this paper, we present Artificial Accurate Time-stamp (AAT), a machine-learning-based software method to improve the accuracy of hardware time-stamps beyond what is provided by the underlying hardware design. We successfully apply this method to remove time-stamping errors caused by varying network traffic patterns and queue variance in the network adapter. Additionally, we demonstrate a real-world application that uses this method to reduce network synchronization error, from over 300 nanoseconds to lower than 30 nanoseconds (a 10x reduction), without changing the hardware or any software in the application layer.

关键词： Measurement errors Telecommunication traffic Hardware Software Synchronization

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Multi-Core parallel processing Technique to Prepare the Time Series Data for the Early Detection of DDoS Flooding Attacks

Multi-Core Parallel Processing Technique to Prepare the Time...

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International conference on Computing for Sustainable Global Development (INDIACom)

作者： S. Ratan Kumar V. Valli Kumari K. V. S. V. N. Raju Anil Neerukonda Institute of Technology and Sciences Visakhapatnam India Andhra University Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Visakhapatnam India

distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential processing Techniques. This paper proposes a Multi-Core parallel processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing.

关键词： Cloud computing Time series analysis distributed databases parallel processing Denial-of-service attack Floods Internet of Things

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A New Structure Of Handwritten Number Recognition : ––based on MNIST

A New Structure Of Handwritten Number Recognition : ––Base...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Weiqiao Yao Yulan He Ruhua Lu College of Software and Communication Engineering Xiangnan University Chenzhou Hunan China

ISBN: (纸本)9781665417396

MNIST handwritten number recognition is processed by building and using a single linear feedforward neuron. Ten handwritten digital images of 0–9 are input into the specified position as samples, and then the model generated by the machine learning is allowed to automatically distinguish the numbers displayed by the current image, and the corresponding numbers are correctly identified and outputted. The main thing is to process the image through unique thermal coding and label, and then to get a set of data after processing the data by a specific function. Then a linear feedforward [1] model is defined. After simple processing of the image through a series of functions and specific coding, the data obtained by the specified function can get a similarity result between the corresponding predicted value and the label value through the specified function. Finally, the output similarity results are classified by the main function softmax to achieve the recognition results. Similar to the neurons in the human brain neural network, neurons, as the main existence basic structure of the human brain, play a self-evident and important role in the human body. It can be seen that the existence and status of neurons can’t be replaced in the profound change of machine artificial intelligence, and its further improvement and optimization also has great research value and significance.

关键词： Training Earth Handwriting recognition Information science Image coding Neurons Machine learning

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