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High-Performance Packet processing and Measurements 10

High-Performance Packet Processing and Measurements

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10th international conference on Communication Systems and Networks (COMSNETS)

作者： Gallenmueller, Sebastian Scholz, Dominik Wohlfart, Florian Scheitle, Quirin Emmerich, Paul Carle, Georg Tech Univ Munich Dept Informat Chair Network Architectures & Serv Munich Germany

ISBN: (纸本)9781538611821

Networks and network architectures are constantly evolving, manifesting in new developments such as SDN, P4, and 400G Ethernet. these novel paradigms and technologies require network researchers to investigate and to adapt their measurement facilities. We present two tools which can foster this process. the first tool, pos, supports a fully automated workflow for performing and evaluating network experiments. One of its key features is the testbed orchestration to maintain and recreate a specified network test environment gaining reproducible experiment results. the second tool, libmoon, is a user-friendly userspace packet processing framework based on DPDK. Among many other projects, libmoon powers MoonGen, a dedicated packet generator in broad use by the community. MoonGen's hardware-supported generation and measurement capabilities are central to our network experiments to reliably recreate measurements. Further, a survey of scientific publications and applications presents projects based on both libmoon and Moon Gen. We argue that combining pos and libmoon/MoonGen creates an ideal platform for network experiments. this platform offers an affordable price, high flexibility, ease of use and generation of reproducible experiments.

关键词： reproducible network measurements high-performance packet processing libmoon MoonGen

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parallel processing of Packets with a PRAM 18

Parallel Processing of Packets with a PRAM

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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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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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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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Trends of Intel MIC Application In Bioinformatics

Trends of Intel MIC Application In Bioinformatics

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Xinyi Wang Cangshuai Wu Zhen Huang Collage of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

With the rapid development of next-generation sequencing (NGS) technology, the ever-increasing biological sequence data poses a tremendous challenge to data processing. therefore, there is an urgent need for intensive computing power to speed up the data analysis process. Among the state-of-the-art parallel accelerators, Intel Xeon Phi coprocessor is a bootable host processor based on Intel Many Integrated Core (MIC) architecture that provides massive parallelism and vectorization to support the most demanding high-performance computing (HPC) applications. the underlying x86 architecture supports common parallel programming standard libraries that provide familiarity and flexibility to transplant existing code to heterogeneous computing environments. In addition, it delivers three usage model including native, offload and symmetric models to solve different application problems on the MIC-based neo-heterogeneous architectures. Currently, Intel Xeon Phi is becoming a common parallel computing platform for decreasing the computational cost of the most demanding processes in bioinformatics. To help researchers make better use of MIC, we reviewed the MIC-based bioinformatics applications, providing a comprehensive guideline for bioinformatics researchers to apply MIC in their own fields.

关键词： Microwave integrated circuits Computational modeling Bioinformatics Coprocessors Computer architecture parallel processing Acceleration

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

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

引用

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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Efficient algorithms of parallel Skyline Join over Data Streams 18th

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

引用

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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Compression and Transfer of Images in Wireless Sensor Networks Using the Transformation of Residue Number System

Compression and Transfer of Images in Wireless Sensor Networ...

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IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： Vasyl Yatskiv Anatoliy Sachenko Nataliya Yatskiv Pavlo Bykovyy Andriy Segin Ternopil National Economic University

ISBN: (纸本)9781728140704

the paper proposes a new approach to compression and data transmission in Internet of things environment, based on the transformation of the Residue Number System and the method of arithmetic coding. the transformation of the Residue Number System is used to divide the data into parts. As a result, the received remainders are compressing in parallel used the arithmetic coding. then they are transmitting through parallel routes, if the last ones are present. the proposed approach improves the processing speed through the parallel work and provides a compression ratio of depending on the type of data.

关键词： Wireless sensor networks Compression Residue number system residue number systems Wireless sensor networks compression compression ratio parallel Lines images arithmetic coding

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Two-Dimensional DOA Estimation with Modified parallel Coprime Linear Sub-Arrays 10

Two-Dimensional DOA Estimation with Modified Parallel Coprim...

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10th international conference on Wireless Communications and Signal processing (WCSP)

作者： Pandav, Sanjay Ubaidulla, P. Def Elect Res Lab Hyderabad Telangana India IIIT Hyderabad Telangana India

ISBN: (纸本)9781538661192

Two dimensional (2-D) direction of arrival (DOA) estimation is a fundamental problem in array signal processing with wide range of applications. In this paper, an approach to 2-D DOA estimation with a modified parallel coprime linear subarrays using MUSIC algorithm and least squares is proposed. A virtual difference co-array is synthesized by vectorizing the cross-covariance matrix of sub-array data. In the proposed method, the 2-D DOA estimation problem is decomposed as two one dimensional (1-D) DOA estimation problems in which azimuth and elevation DOAs are estimated and automatically paired using MUSIC and Least Squares (LS). Simulation results are presented to verify the performance of the proposed method and the improvements resulting from the proposed array geometry.

关键词： Direction-of-arrival estimation Estimation Multiple signal classification Azimuth Geometry Arrays Signal to noise ratio

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