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parallel implementation of nearest feature line and rectified nearest feature line segment classifiers using openmp 17th

Parallel implementation of nearest feature line and rectifie...

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17th international symposium on distributed computing and Artificial Intelligence, DCAI 2020

作者： Uribe-Hurtado, Ana-Lorena Villegas-Jaramillo, Eduardo-José Orozco-Alzate, Mauricio Departamento de Informática y Computación Universidad Nacional de Colombia - Sede Manizales Kilómetro 7 vía al Magdalena Manizales170003 Colombia

ISBN: (纸本)9783030530358

parallelizing computationally expensive classification algorithms, such as the Rectified Nearest Feature Line Segment (RNFLS), remains a task for expert programmers due to the complexity involved in rewriting the application and the required knowledge of the available hardware and tools. A simple parallel implementation of the Nearest Feature Line (NFL) and the RNFLS algorithms using OpenMP over multicore architectures is presented. Both non-parametric classifiers are used in the classification of datasets that contain few samples. The training and testing evaluation technique is used, with a 70–30 ratio respectively and seven datasets from the UCI repository, to verify the speedup and the accuracy of the classifier. Results and experiments derived from the parallel execution of both algorithms, calculating the average of 20 repetitions on a small architecture of 6 physical cores and 12 ones with multi-threading, show that accelerations of up to 21 times can be achieved with NFL and up to 13 times with RNFLS. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Classification (of information)

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HPDC 2016 - Proceedings of the 25th ACM international symposium on High-Performance parallel and distributed computing

HPDC 2016 - Proceedings of the 25th ACM International Sympos...

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25th ACM international symposium on High-Performance parallel and distributed computing, HPDC 2016

ISBN: (纸本)9781450343145

The proceedings contain 28 papers. The topics discussed include: routing on the dependency graph: a new approach to deadlock-free high-performance routing;towards practical algorithm based fault tolerance in dense linear algebra;new-sum: a novel online ABFT scheme for general iterative methods;SDS-Sort: scalable dynamic skew-aware parallel sorting;SWAT: a programmable, in-memory, distributed, high-performance computing platform;consecutive job submission behavior at Mira supercomputer;with extreme scale computing the rules have changed;NVL-C: static analysis techniques for efficient, correct programming of non-volatile main memory systems;algorithm-directed data placement in explicitly managed non-volatile memory;self-configuring software-defined overlay bypass for seamless inter- and intra-cloud virtual networking;Wiera: towards flexible multi-tiered geo-distributed cloud storage instances;IMPACC: a tightly integrated MPI+OpenACC framework exploiting shared memory parallelism;faster and cheaper: parallelizing large-scale matrix factorization on GPUs;implications of heterogeneous memories in next generation server systems;DD-Graph: a highly cost-effective distributed disk-based graph-processing framework;evaluation of pattern matching workloads in graph analysis systems;and BAShuffler: maximizing network bandwidth utilization in the shuffle of YARN.

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FDRA: Fully distributed Routing Architecture for Private Virtual Network in Public Cloud 11th

FDRA: Fully Distributed Routing Architecture for Private Vir...

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11th international symposium on parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Hu, Zhangfeng Zhang, Hui Sun, Siqing Gao, Chuanji Li, Yanjun Li, Xiong Cloud Computing Research Center Inspur Cloud Service Group Beijing China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9789811600098

A virtual private cloud (VPC) is often comprised of a set of virtual computing, storage and network resource which is allocated from a public cloud. Public clouds build virtualized resource pools based on physical infrastructure including generic x86 servers, network devices (e.g. switches, routers, firewalls), storage servers and so forth to dynamically provision virtual computing and storage resource to customers, while virtual network is a bridge that connects all the computing resource in a VPC and segregates network traffic between different VPCs. One VPC may contain multiple subnets, which makes private virtual network should provide the capability of communications between virtual computing resources i.e. virtual machines’ communication through Layer-2 switching and Layer-3 routing simultaneously. In this paper we propose a fully distributed routing architecture (FDRA) for private virtual network to fulfill the requirements of public cloud. FDRA splits the VPC traffic into two categories, i.e. the traffic goes inside of a VPC and the traffic goes out of a VPC, and presents two different routing entities to route different traffic issued from virtual machines in a VPC. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization

A Framework to Exploit Data Sparsity in Tile Low-Rank Choles...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Qinglei Cao Rabab Alomairy Yu Pei George Bosilca Hatem Ltaief David Keyes Jack Dongarra Innovative Computing Laboratory University of Tennessee US Division of Computer Electrical and Mathematical Sciences and Engineering Extreme Computing Research Center King Abdullah University of Science and Technology KSA The Oak Ridge National Laboratory US University of Manchester UK

We present a general framework that couples the PaRSEC runtime system and the HiCMA numerical library to solve challenging 3D data-sparse problems. Though formally dense, many matrix operators possess a rank structured property that can be exploited during the most time-consuming computational phase, i.e., the matrix factorization. In particular, this work highlights how a software bundle powered by a task-based programming model can address the heterogeneous workloads engendered by compressing the dense operator. Using Tile Low-Rank (TLR) approximation, our approach consists in capturing the most significant information in each tile of the matrix using a threshold which satisfies the application's accuracy requirements. Matrix operations are performed on the compressed data layout, reducing memory footprint and algorithmic complexity. Our proposed software solution accommodates a range of traditional data structures of linear algebra, i.e., from dense and data-sparse to sparse, within a single matrix operation. Separation of concerns is at the heart: hardware-agnostic implementation, asynchronous execution with a dynamic runtime system, and high performance numerical kernels, to prepare scientific applications to embrace exascale opportunities. This ambition necessitates extensions to PaRSEC that incorporate information related to data structure and rank distribution into the runtime decision-making. We introduce two runtime optimizations to address the challenges encountered when confronted with a large rank disparity: (1) a trimming procedure performed at runtime to cut away data dependencies from the directed acyclic graph discovered to be no longer required after compression and (2) a rank-aware diamond-shaped data distribution to mitigate the load imbalance overheads, reduce data movement, and conserve memory foot-print. We assess our implementation using 3D unstructured mesh deformation based on Radial Basis Function (RBF) interpolation. We report performance

关键词： Runtime Three-dimensional displays Programming Data structures Software Matrices Libraries

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EXPPO: EXecution Performance Profiling and Optimization for CPS Co-simulation-as-a-Service 23

EXPPO: EXecution Performance Profiling and Optimization for ...

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IEEE 23rd international symposium on Real-Time distributed computing (ISORC)

作者： Barve, Yogesh D. Neema, Himanshu Kang, Zhuangwei Sun, Hongyang Gokhale, Aniruddha Roth, Thomas Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37212 USA NIST Smart Grid & Cyber Phys Syst Program Off Engn Lab Gaithersburg MD 20899 USA

ISBN: (纸本)9781728169583

A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost.

关键词： cyber-physical systems distributed simulation cloud computing latency performance resource management gang scheduling

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A Work-Time Optimal parallel Exhaustive Search Algorithm for the QUBO and the Ising model, with GPU implementation 34

A Work-Time Optimal Parallel Exhaustive Search Algorithm for...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Tao, Masaki Nakano, Koji Ito, Yasuaki Yasudo, Ryota Tatekawa, Masaru Katsuki, Ryota Yazane, Takashi Inaba, Yoko Hiroshima Univ Dept Informat Engn Kagamiyama 1-4-1 Higashihiroshima 7398527 Japan NTT DATA Corp Res & Dev Headquarters Koto Ku Toyosu Ctr BldgAnnex 3-9Toyosu 3 Chome Tokyo 1358671 Japan

ISBN: (纸本)9781728174457

The main contribution of this paper is to present a simple exhaustive search algorithm for the quadratic un-constraint binary optimization (QUBO) problem. It computes the values of the objective function E(X) for all n-bit input vector X in O(2(n)) time. Since Omega(2(n)) time is necessary to output E(X) for all 2(n) vectors X, this sequential algorithm is optimal. We also present a work-time optimal parallel algorithm running O(log n) time using 2(n)/log n processors on the CREW-PRAM. This parallel algorithm is work optimal, because the total number of computational operations is equal to the running time of the optimal sequential algorithm. Also, it is time optimal because any parallel algorithm using any large number of processors takes at least Omega(log n) time for evaluating E(X). Further, we have implemented this parallel algorithm to run on the GPU. The experimental results on NVIDIA GeForce RTX 2080Ti GPU show that our GPU implementation runs more than 1000 times faster than the sequential algorithm running on Intel Corei7-8700K CPU(3.70GHz) for the QUBO with n-bit vector whenever n >= 33. We also compare our exhaustive search parallel algorithm with several non-exhaustive search approaches for solving the QUBO including D-Wave 2000Q quantum annealer, simulated annealing algorithm, and Gurobi optimizer.

关键词： Quantum computing Ising model QUBO Combinatorial optimization GPGPU

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Honeyeye: A Network Traffic Collection Framework for distributed ICS Honeynets 18

Honeyeye: A Network Traffic Collection Framework for Distrib...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Sheng, Chuan Yao, Yu Li, Dongbiao An, Hongna Yang, Wei Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781665414852

The honeynet, as an important security resource, is increasingly used in the industrial field in order to detect, analyze, and prevent network attacks against industrial control systems (ICSs). However, conventional network traffic collection methods used in honeynets cannot meet the more and more complex requirements of large-scale and distributed honeynets. This paper presents a new network traffic collection framework for distributed ICS honeynets called Honeyeye. Honeyeye can provide some different running modes for different application scenarios and purposes. Honeyeye can not only collect and save network traffic, but also parse it into readable data and convert it into the required format. By this way, Honeyeye tends to provide network administrators and intrusion detection systems (IDSs) with more understandable and directly available data rather than captured opaque binary data. Experimental results show that the framework is effective in parsing, converting, and transmitting ICS honeynet traffic.

关键词： Industrial Control System distributed ICS Honeynet Network Traffic Collection

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Mobile Edge computing for Intelligent Mining Safety: A Case Study of Ventilator 18

Mobile Edge Computing for Intelligent Mining Safety: A Case ...

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作者： Zhang, Yuchun Zhao, Liang Yang, Kaiqi Xu, Lexi China Coal Technol & Engn Grp Shenyang Res Inst State Key Lab Coal Mine Safety Technol Fushun 113122 Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China China Unicom Beijing Peoples R China

ISBN: (纸本)9781665414852

The underground mining field sometimes is uncertain, which may lead to the severity of incidents, including explosion, corruption, and toxic air. To this end, it is vital to monitor and analyze the possibility of incidents and promote the corresponding rescue actions. It is still challenging to realize real-time data processing, in which the deployment of large computing devices is facing problems, including energy, space, and surrounding dusty environment. Besides, due to the complex environment, transmitting the collected data to the remote cloud can suffer from the inevitable high latency under the current communication technology, in which suffer. By bringing the server to the location closed to the miner, mobile edge computing (MEC) is a promising distributed computation architecture to seize the opportunity of enabling low-cost, low-latency collection and processing. In the underground mining field, miners' life status should be monitored in real-time In this context, MEC is a right candidate for such a scenario. In this paper, we discuss the utilization of MEC for analyzing miner's body condition. We also show the challenges and open issues of such an application. Also, we give a case study of using MEC-enabled miner health platform to assist the ventilator in the underground field.

关键词： mobile edge computing mining industry miner health ventilator

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HPDC 2021 Welcome Message

HPDC 2021 - Proceedings of the 30th International Symposium ...

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HPDC 2021 - Proceedings of the 30th international symposium on High-Performance parallel and distributed computing 2021年 III-IV页

作者： Markidis, Stefano Laure, Erwin Varbanescu, Ana Lucia Lofstead, Gerald Fredrick Jay KTH Royal Institute of Technology Stockholm Sweden Max Planck Computing and Data Facility Germany University of Amsterdam Netherlands Sandia National Laboratories United States

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Optimal Decomposable QAM Constellations for Minimizing Symbol-error Probability

Optimal Decomposable QAM Constellations for Minimizing Symbo...

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IEEE international symposium on parallel and distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Brett Wiens Daniel C. Lee School of Engineering Science Simon Fraser University Burnaby Canada

This paper presents signal space constellations that minimize symbol-error probability in an uncoded communications system. We develop a modified projected gradient optimization method to solve for these constellations that scales efficiently, enabling the optimization of very large constellations. The optimized constellation shapes show significant improvements in symbol-error probability over a wide range of channel conditions compared with uniformly-spaced constellations. At high SNR, small deviations from uniform spacing can make substantial improvements in system performance. These results have promising applications to low-complexity systems with real-time data transmission requirements.

关键词： Gradient methods Limiting Shape Quadrature amplitude modulation System performance Symbols Ubiquitous computing

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