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parallel solution of sparse problems by using a sequence of large dense blocks 3rd

Proceedings of the 1996 3rd International Workshop on Applied parallel computing, PARA

作者： Ostromsky, Tz. Salvini, S. Wasniewski, J. Zlatev, Z. Central Laboratory for Parallel Information Processing Bulgarian Academy of Sciences Acad. G.Bonchev str. bl. 25-A Sofia 1113 Bulgaria The Numerical Algorithms Group Ltd. (NAG) Wilkinson House Jordan Hill Road Oxford OX2 8DR United Kingdom Danish Computing Centre for Research and Education (UNI-C) DTU Bldg. 304 Lyngby DK-2800 Denmark National Environmental Research Institute Frederiksborgvej 399 P. O. Box 358 Roskilde DK-4000 Denmark

ISBN: (纸本)3540620958

Linear least squares problems arise in many important fields of science and engineering (such as econometry, geodesy, statistics, structural analysis, fluid dynamics, etc.). Applications from all these fields are to be handled numerically in the solution of various models used in the development of new industrial products. At the National Environmental research Institute (Denmark), linear least squares problems have been applied in the efforts to determine optimal values of some of the parameters involved in air pollution models. Large and sparse least squares problems can be handled by treating a sequence of dense blocks. Standard parallel subroutines, which perform orthogonal decomposition of dense matrices (as, for example, subroutines from LAPACK, SCALAPACK or NAG), can be used to handle successively the dense blocks. A preliminary reordering procedure, LORA, is to be applied at the beginning of the computational process. The size of the blocks can be adjusted to the particular computer used. Results obtained on a Silicon Graphics POWER CHALLENGE are given. The same method can also be used in the solution of large and sparse systems of linear algebraic equations. © Springer-Verlag Berlin Heidelberg 1996.

关键词： Linear algebra

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Synchronizing the Timestamps of Concurrent Events in Traces of Hybrid MPI/OpenMP Applications

Synchronizing the Timestamps of Concurrent Events in Traces ...

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IEEE International Conference on Cluster computing

作者： Daniel Becker Markus Geimer Rolf Rabenseifner Felix Wolf Department of Computer Science RWTH Aachen University Aachen Germany German Research School for Simulation Sciences Laboratory for Parallel Programming Aachen Germany Forschungszentrum Jülich Jülich Supercomputing Centre Jülich Germany University of Stuttgart High Performance Computing-Center Stuttgart Germany

Event traces are helpful in understanding the performance behavior of parallel applications since they allow the in-depth analysis of communication and synchronization patterns. However, the absence of synchronized clocks on most cluster systems may render the analysis ineffective because inaccurate relative event timings may misrepresent the logical event order and lead to errors when quantifying the impact of certain behaviors or confuse the users of time-line visualization tools by showing messages flowing backward in time. In our earlier work, we have developed a scalable algorithm that eliminates inconsistent inter-process timings postmortem in traces of pure MPI applications. Since hybrid programming, the combination of MPI and OpenMP in a single application, is becoming more popular on clusters in response to rising numbers of cores per chip and widening shared-memory nodes, we present an extended version of the algorithm that in addition to message-passing event semantics also preserves and restores shared-memory event semantics.

关键词： Clocks Message systems Semantics Synchronization Algorithm design and analysis Interpolation

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MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI C...

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Annual Conference on Privacy, Security and Trust, PST

作者： Yihao Yang Xiaolei Dong Jiachen Shen Zhenfu Cao Yunbo Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Zongyang Hou Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan Print-Rite Technology Development Co. Ltd. Of Zhuhai China

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 20 on server side and 2 16 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.

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Generalised hermite functions and their applications in spectral approximations

arXiv

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arXiv 2020年

作者： Sheng, Changtao Ma, Suna Li, Huiyuan Wang, Li-Lian Jia, Lueling Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore School of Mathematical Sciences Peking University Beijing100871 China State Key Laboratory of Computer Science/Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China Beijing Computational Science Research Center Beijing100193 China

In 1939, G. Szegö first introduced a family of generalised Hermite polynomials (GHPs) as a generalisation of usual Hermite polynomials, which are orthogonal with respect to the weight function |x|2µe´x2, µ ą ´ 12 on the whole line. Since then, there have been a few works on the study of their properties, but no any on their applications to numerical solutions of partial differential equations (PDEs). The main purposes of this paper are twofold. The first is to construct the generalised Hermite polynomials and generalised Hermite functions (GHFs) in arbitrary d dimensions, which are orthogonal with respect to |x|2µe´|x|2 and |x|2µ in Rd, respectively. We then define a family of adjoint generalised Hermite functions (A-GHFs) upon GHFs, which has two appealing properties: (i) the Fourier transform maps A-GHF to the corresponding GHF;and (ii) A-GHFs are orthogonal with respect to the inner product ru, vsHspRdq "pṕ∆q 2s u, ṕ∆q 2s vqRd associated with the integral fractional Laplacian. The second purpose is to explore their applications in spectral approximations of PDEs. As a remarkable consequence of the fractional Sobolev-type orthogonality, the spectral-Galerkin method using A-GHFs as basis functions leads to an identity stiffness matrix for the integral fractional Laplacian operator ṕ∆qs, which is known to be notoriously difficult and expensive to discretise. Indeed, the A-GHFs provide an optimal basis for Hermite approximation of ṕ∆qs in Rd. As a by-product, the A-GHFs with integer parameter s can significantly improve the existing Hermite spectral-Galerkin algorithms for PDEs with usual Laplacian. We also conduct rigorous error analysis of approximation by GHFs and of the optimal spectral method for fractional PDEs in Rd. The second application is to solve the eigenvalue problem of the Schrödinger operator involving a general fractional power potential. Following the same spirit, we further introduce a Müntz-type GHFs that are orthogonal with respect to an inner pr

关键词： Stiffness matrix

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TransUPR: A Transformer-based Plug-and-Play Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation

TransUPR: A Transformer-based Plug-and-Play Uncertain Point ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zifan Yu Meida Chen Zhikang Zhang Suya You Raghuveer Rao Sanjeev Agarwal Fengbo Ren Parallel Systems and Computing Laboratory Arizona State University Tempe Arizona USA Institute for Creatives Technologies University of Southern University Los Angeles California USA U.S. DEVCOM Army Research Laboratory Adelphi Maryland USA U.S. DEVCOM Army C5ISR Center Fort Belvoir Virginia USA

Common image-based LiDAR point cloud semantic segmentation (LiDAR PCSS) approaches have bottlenecks resulting from the boundary-blurring problem of convolution neural networks (CNNs) and quantitation loss of spherical projection. In this work, we propose a transformer-based plug-and-play uncertain point refiner, i.e., TransUPR, to refine selected uncertain points in a learnable manner, which leads to an improved segmentation performance. Uncertain points are sampled from coarse semantic segmentation results of 2D image segmentation where uncertain points are located close to the object boundaries in the 2D range image representation and 3D spherical projection background points. Following that, the geometry and coarse semantic features of uncertain points are aggregated by neighbor points in 3D space without adding expensive computation and memory footprint. Finally, the transformer-based refiner, which contains four stacked self-attention layers, along with an MLP module, is utilized for uncertain point classification on the concatenated features of self-attention layers. As the proposed refiner is independent of 2D CNNs, our TransUPR can be easily integrated into any existing image-based LiDAR PCSS approaches, e.g., CENet. Our TransUPR with the CENet achieves state-of-the-art performance, i.e., 68.2% mean Intersection over Union (mIoU) on the Semantic KITTI benchmark, which provides a performance improvement of 0.6% on the mIoU compared to the original CENet.

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A massively parallel adaptive finite element method with dynamic load balancing 93

A massively parallel adaptive finite element method with dyn...

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Proceedings of the 1993 ACM/IEEE conference on Supercomputing

作者： K. D. Devine J. E. Flaherty S. R. Wheat A. B. Maccabe Department of Computer Science Rensselaer Polytechnic Institute Troy NY and Sandia National Laboratories Massively Parallel Computing Research Laboratory Dept. 1424 Sandia National Laboratories Albuquerque NM and Sandia National Laboratories Department of Computer Science The University of New Mexico Albuquerque NM and Sandia National Laboratories

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FPGA-Based parallel Implementation of SURF Algorithm

FPGA-Based Parallel Implementation of SURF Algorithm

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International Conference on parallel and Distributed Systems (ICPADS)

作者： Wenjie Chen Shuaishuai Ding Zhilei Chai Daojing He Weihua Zhang Guanhua Zhang Qiwei Peng Wang Luo MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China School of loT Engineering Jiangnan University Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Parallel Processing Institute Fudan University Shanghai China

ISBN: (纸本)9781509053827

SURF (Speeded up robust features) detection is used extensively in object detection, tracking and matching. However, due to its high complexity, it is usually a challenge to perform such detection in real time on a general-purpose processor. This paper proposes a parallel computing algorithm for the fast computation of SURF, which is specially designed for FPGAs. By efficiently exploiting the advantages of the architecture of an FPGA, and by appropriately handling the inherent parallelism of the SURF computation, the proposed algorithm is able to significantly reduce the computation time. Our experimental results show that, for an image with a resolution of 640x480, the processing time for computing using SURF is only 0.047 seconds on an FPGA (XC6SLX150T, 66.7 MHz), which is 13 times faster than when performed on a typical i3-3240 CPU (with a 3.4 GHz main frequency) and 249 times faster than when performed on a traditional ARM system (CortexTM-A8, 1 GHz).

关键词： Feature extraction parallel processing Field programmable gate arrays Hardware Algorithm design and analysis Interpolation Real-time systems

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A Fault Classification Prediction Model Based on Loose-Type Wavelet Neural Network for Server Health Management

A Fault Classification Prediction Model Based on Loose-Type ...

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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Xiaoqi Lu Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers.

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Architecture and Key Technologies of parallel Dispatching System for Railway Technical Operation Stations

Architecture and Key Technologies of Parallel Dispatching Sy...

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2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Xiong, Gang Yang, Donghu Xu, Wei Li, Runmei Chen, Shichao Song, Bing Dong, Xisong Zhu, Fenghua Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Signal and Communication Research Institute China Academy of Railway Science Corporation Limited Beijing100081 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100081 China Beijing Huairou Academy of Parallel Sensing Beijing101400 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China

ISBN: (纸本)9798350309003

A railroad technical operation station is an extensive and complex operation system and a variety of random disturbing factors further increase the complexity of its command and dispatching. To enhance the railroad technical operation stations' intelligence, and integration level, and enhance its dispatching management ability of complex operation plans, based on the ACP approach, this paper designs the parallel dispatching system (PDS) architecture, and describes its key technologies including the artificial dispatching system (ADS), computing experiments, parallel execution, etc. On the basis of the artificial dispatching system, computational experiments can optimize various types of technical operations and the overall operational process, to achieve parallel execution between the actual dispatching system and ADS. PDS can provide more efficient and intelligent command and dispatching solutions for railroad technical work stations, and promote the development of the railroad transportation industry in the direction of higher quality and efficiency. © 2023 IEEE.

关键词： Railroads

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A Deep Belief Networks Intrusion Detection Method Based on Generative Adversarial Networks

A Deep Belief Networks Intrusion Detection Method Based on G...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Zezhang Zheng Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing School of Computer Electronics and Information Guangxi University (Guangxi University) Nanning China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorithms are constantly improving in terms of accuracy, their stability in the face of insufficient attack samples is a major obstacle. To solve the issues of insufficient attack samples and low detection accuracy in network intrusion detection, this paper proposes a deep confidence network intrusion detection method G-DBN based on GAN. The model is based on the malicious sample extension of the generative adversarial network, and it can produce adversarial samples using malicious network flows as original samples. Furthermore, this paper uses deep belief network technology to create and assess the efficacy of the G-DBN model in detecting network attacks, comparing it to standard DBN models and other network intrusion detection techniques. Experimental results show that compared to the standard three-layer DBN method, the G-DBN method described in this paper improves the detection accuracy of attack samples by 6.46% and better meets the performance requirements of current practical applications.

关键词： Seminars Learning systems Data centers Accuracy Network intrusion detection Network security Generative adversarial networks

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