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Continuous and parallel LiDAR Point-cloud Clustering 38

Continuous and Parallel LiDAR Point-cloud Clustering

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38th IEEE international conference on Distributed Computing Systems (ICDCS)

作者： Najdataei, Hannaneh Nikolakopoulos, Yiannis Gulisano, Vincenzo Papatriantafilou, Marina Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781538668719

In distributed digitalized environments in the context of the Internet of things, we often need to do an analysis of big data originating at high rate-sensors at the edge of the infrastructure. A characteristic example is the light detection and ranging (LiDAR) technology, that allows sensing surrounding objects with fine-grained resolution in large areas. their data (known as point clouds), generated continuously at very high rates, through appropriate analysis can provide information to support automated functionality in distributed cyber-physical systems;clustering of point clouds is a key problem to extract this type of information. Methods for solving the problem in a continuous fashion can facilitate improved processing in fog architectures, through enabling low-latency, efficient continuous and streaming processing of data close to the sources;moreover, parallelism is a key requirement to exploit a variety of computing architectures in this context. We propose Lisco, a single-pass continuous Euclidean-distance-based clustering of LiDAR point clouds, that maximizes the granularity of the data processing pipeline and thus shows the potential for data- and pipeline-parallelism. We further present its parallel version, P-Lisco, that is architecture-independent and exploits the parallelism revealed by Lisco's algorithmic approach. Besides their algorithmic analysis, we provide a thorough experimental evaluation on architectures representative of high-end servers and of resource-constrained embedded devices and highlight the multiplicative improvements and scalability benefits of the proposed algorithms compared to the baseline, using both real-world datasets as well as synthetic ones to fully explore a wide spectrum of stress levels for the algorithms.

关键词： streaming parallel clustering point-cloud Li-DAR edge computing fog computing data analysis

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parallel processing of Packets with a PRAM 18

Parallel Processing of Packets with a PRAM

引用

19th international conference on Distributed Computing and Networking (ICDCN)

作者： Acharya, H. B. Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781450363723

the operation of policy tables, for access control (firewalls) and for routing (flow tables), is a major component in networks. For example, one of the most important bottlenecks in the Internet is the time taken by backbone routers to resolve packets. In practice, such routers use special hardware such as Ternary Content-Addressable Memory. How well do standard architectures for parallel computing work to speed up such operations? At first glance, it appears that first match policies such as firewalls might force a serial approach, where the only mechanism for speeding up packet processing is to handle different packets on different cores. However, we show in this paper that the operation of packet-processing policies for each individual packet can in fact be sped up with a PRAM parallel computer. We go on to demonstrate how to build a policy engine for XMT (Explicit Multi threading), a practical near-PRAM architecture and instruction set, and present some performance results.

关键词： parallel algorithms XMT architecture fast packet processing flow table firewall middlebox

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A parallel Iterative Approach for Efficient Full Chip thermal Analysis 7

A Parallel Iterative Approach for Efficient Full Chip Therma...

引用

7th international conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Floros, George Daloukas, Konstantis Evmorfopoulos, Nestor Stamoulis, George Univ Thessaly Dept Elect & Comp Engn Volos Greece Helic Inc 2350 Mission Coll BlvdSuite 495 Santa Clara CA 95054 USA

ISBN: (纸本)9781538647882

Efficient full-chip thermal simulation is among the most challenging problems facing the EDA industry today, especially for modern 3D integrated circuits, due to the huge linear systems resulting from thermal modeling approaches that require unreasonably long computational times. Direct linear solvers are not capable of handling such huge problems and iterative methods are the only feasible approach. In this paper, we propose a computationally efficient iterative method with a parallel preconditioned technique that exploits the resources of massively parallel architectures such as Graphic Processor Units (GPUs). Experimental results, demonstrate that the proposed method achieves a speedup of 2.2X in CPU execution and a 26.93X speedup in GPU execution over the state of the art iterative method.

关键词： Manganese Transforms Mathematical model Integrated circuit modeling thermal analysis Computational modeling three-dimensional displays

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Genetic and practical swarm optimisation algorithms for patient-specific seizure detection systems 4

Genetic and practical swarm optimisation algorithms for pati...

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4th international conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： Ammar, Sabrina Trigui, Omar Senouci, Mohamed Ibn Khaldoun Univ Tiaret LIM Tiaret Algeria Sfax Univ ENIS ATMS Sfax Tunisia Oran Univ Ahmed Ben Bella Lab Parallel & Embedded Architectures & Intens Co Oran Algeria

ISBN: (纸本)9781538652398

the automatic seizure detection system is designed to aid the physician's decision-making process with recognizing the sought EEG segments. Increasing the system sensitivity is the goal of several studies. In fact, ameliorating this criterion allows to find the same interpretations as found with a visual scanning A patient-specific system is able to set its optimal parameters according to the patient which makes it more accurate than non patient-specific system. this paper introduces a new patient specific system with genetic and practical swarm optimisation algorithms. the results show that the proposed system is able to reach acceptable performances. Moreover, the use of the genetic algorithm improves the system sensitivity (95%) more than the practical swarm optimization (91%) which makes it a better method for the system parameter optimisation.

关键词： Epilepsy Seizures Detection Patient-Specific Seizure Detection Systems Genetic Algorithm Swarm Optimisation Algorithm

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Log(Graph): A Near-Optimal High-Performance Graph Representation 18

Log(Graph): A Near-Optimal High-Performance Graph Representa...

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27th IEEE/ACM/IFIP international conference on parallel architectures and Compilation Techniques (PACT)

作者： Besta, Maciej Stanojevic, Dimitri Zivic, Tijana Singh, Jagpreet Hoerold, Maurice Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781450359863

Today's graphs used in domains such as machine learning or social network analysis may contain hundreds of billions of edges. Yet, they are not necessarily stored efficiently, and standard graph representations such as adjacency lists waste a significant number of bits while graph compression schemes such as WebGraph often require time-consuming decompression. To address this, we propose Log(Graph): a graph representation that combines high compression ratios with very low-overhead decompression to enable cheaper and faster graph processing. the key idea is to encode a graph so that the parts of the representation approach or match the respective storage lower bounds. We call our approach "graph logarithmization" because these bounds are usually logarithmic. Our high-performance Log(Graph) implementation based on modern bitwise operations and state-of-the-art succinct data structures achieves high compression ratios as well as performance. For example, compared to the tuned Graph Algorithm processing Benchmark Suite (GAPBS), it reduces graph sizes by 20-35% while matching GAPBS' performance or even delivering speedups due to reducing amounts of transferred data. It approaches the compression ratio of the established WebGraph compression library while enabling speedups of up to more than 2x. Log(Graph) can improve the design of various graph processing engines or libraries on single NUMA nodes as well as distributed-memory systems.

关键词： graph compression graph representation graph layout parallel graph algorithms ILP succinct data structures

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High-Performance Sparse Matrix-Matrix Products on Intel KNL and Multicore architectures 18

High-Performance Sparse Matrix-Matrix Products on Intel KNL ...

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47th international conference on parallel processing (ICPP) / international Workshop on Embedded Multicore Systems (EMS)

作者： Nagasaka, Yusuke Matsuoka, Satoshi Azad, Ariful Buluc, Aydin Tokyo Inst Technol Tokyo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan Lawrence Berkeley Natl Lab Berkeley CA USA Tokyo Inst Technol Dept Math & Comp Sci Tokyo Japan

ISBN: (纸本)9781450365239

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. We firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- and many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. We examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix.

关键词： Sparse matrix SpGEMM Intel KNL

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Accelerating CNN Algorithm with Fine-grained Dataflow architectures 20

Accelerating CNN Algorithm with Fine-grained Dataflow Archit...

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20th IEEE international conference on High Performance Computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on Data Science and Systems (DSS)

作者： Xiang, Taoran Feng, Yujing Ye, Xiaochun Tan, Xu Li, Wenming Zhu, Yatao Wu, Meng Zhang, Hao Fan, Dongrui Chinese Acad Sci ICT State Key Lab Comp Architecture Beijing Peoples R China UCAS Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538666142

Convolutional Neural Network(CNN) is a hot and state-of-the-art algorithm which is widely used in applications such as face recognition, intelligent monitoring, image recognition and text recognition. Because of its high computational complexity, many efficient hardware accelerators have been proposed to exploit high degree of parallel processing for CNN. However, accelerators which are implemented on FPGAs and ASICs usually sacrifice generality for higher performance and lower power consumption. Other accelerators, such as GPUs, are general enough, but they lead to higher power consumption. Fine-grained dataflow architectures, which break conventional Von Neumann architectures, show natural advantages in processing CNN-like algorithms with high computational efficiency and low power consumption. At the same time, it remains broadly applicable and adaptable. In this paper, we propose a scheme for implementing and optimizing CNN on fine-grained dataflow architecture based accelerators. the experiment results reveal that by using our scheme, the performance of AlexNet running on the dataflow accelerator is 3.11x higher than that on NVIDIA Tesla K80, and the power consumption of our hardware is 8.52x lower than that of K80.

关键词： fine-grained dataflow Convolutional Neural Network general accelerator data reuse high parallel

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A Constrained Optimal Model Predictive Control for Mono Inverter Dual parallel PMSM Drives 7

A Constrained Optimal Model Predictive Control for Mono Inve...

引用

7th international conference on Renewable Energy Research and Applications (ICRERA)

作者： Cervone, A. Di Noia, L. P. Rizzo, R. Spina, I Miceli, R. Univ Naples Federico II DIETI Via Claudio 21 I-80125 Naples Italy Univ Palermo DEIM Viale Sci I-90128 Palermo Italy

ISBN: (纸本)9781538659823

the actual trends in the design of AC drives are directed to the reduction of the total weight, volume and cost. Usually, this implies the necessity to adopt new motor topologies and converter architectures. An important role is played by the mono-inverter dual parallel motor (MIDP), which gives the possibility to reduce the total weight and costs of power converters. this paper proposes a novel model predictive control algorithm in order to improve the transient performances of a MIDP used for an overhead carrier. the effectiveness of the proposal control is verified through some numerical simulations.

关键词： AC drives Dual motor Model Predictive Control Constrained Optimization Permanent Magnet Synchronous Machines

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Efficient algorithms of parallel Skyline Join over Data Streams 18th

Efficient Algorithms of Parallel Skyline Join over Data Stre...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zhang, Jinchao Gu, JingZi Cheng, Shuai Li, Bo Wang, Weiping Meng, Dan Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783030050511;9783030050504

the issue of finding skyline tuples over multiple relations, more commonly known as the skyline join problem, has been well studied in scenarios in which the data is static. Most recently, it has become a new trend that performing skyline queries on data streams, where tuples arrive or expire in a continuous approach. A few algorithms have been proposed for computing skylines on two data streams. However, those literatures did not consider the inherent parallelism, or employ serial algorithms to solve the skyline query problem, which cannot leverage the multi-core processors. Based on this motivation, in this paper, we address the problem of parallel computing for skyline join over multiple data streams. We developed a Novel Iterative framework based on the existing work and study the inherent parallelism of the Novel Iterative framework. then we propose two parallel skyline join algorithms over sliding windows, NP-SWJ and IP-SWJ. To the best of our knowledge, this is the first paper that addresses parallel computing of skyline join over multiple data streams. Extensive experimental evaluations on real and synthetic data sets show that the algorithms proposed in this paper provide large gains over the state-of-the-art serial algorithm of skyline join over data streams.

关键词： Skyline join Sliding window Data stream parallel computing

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Collider - parallel Experiments in Silico 19th

Collider - Parallel Experiments in Silico

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19th international GI/ITG conference on Measurement, Modelling and Evaluation of Computing Systems (MMB)

作者： Scheftelowitsch, Dimitri TU Dortmund Informat 4 Dortmund Germany

ISBN: (纸本)9783319749471;9783319749464

Large-scale software experiments are a ubiquitous feature of research. For example, performance evaluation of algorithms implies testing said algorithms on a large number of test cases. We provide a software framework which helps performing experiments on large parameter spaces, benefits from multi-core architectures, and saves generated results in a machine-readable format for future post-processing.

关键词： Performance evaluation Experiments Software tools

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