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18th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2021

作者： He, Kang Barrio, Roberto Chen, Lin Jiang, Hao Liu, Jie Gu, Tongxiang Qi, Jin Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Department of Applied Mathematics University of Zaragoza ZaragozaE50009 Spain College of Computer National University of Defense Technology Changsha410073 China Institute of Applied Physics and Computational Mathematics Beijing100000 China

ISBN: (纸本)9783030935702

Basic recursive summation and common dot product algorithm have a backward error bound that grows linearly with the vector dimension. Blanchard [1] proposed a class of fast and accurate summation and dot product algorithms respectively called FABsum and FABdot, which trades off the calculation accuracy and speed by the block size. Castaldo [2] proposed a multi-layer block summation and dot product algorithm called SuperBlocksum and SuperBlockdot that can increase the accuracy while adding almost no additional calculations. We combine the idea of [1] with the multi-layer block structure to propose SuperFABsum (for "super fast and accurate block summation") and SuperFABdot (for "super fast and accurate block dot product"). Our algorithms have two variants, one is SuperFAB(within), the other is SuperFAB(outside). Our algorithms further improve accuracy and speed compared with FAB and SuperBlock. We conducted accuracy and speed tests on the high-performance FT2000+ processor. Experimental results show that SuperFABdot(within) algorithm is more accurate than FABdot and SuperBlockdot. Compared with FABdot, SuperFABdot(outside) algorithm can achieve up to 1.2 × performance speedup while ensuring similar accuracy. © 2022, IFIP International Federation for Information processing.

关键词： Artificial intelligence

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科学研究的第五范式——以智能驱动的材料设计为例

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Engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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NUMA-aware FFT-based convolution on ARMv8 many-core CPUs

arXiv

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arXiv 2021年

作者： Huang, Xiandong Wang, Qinglin Lu, Shuyu Hao, Ruochen Mei, Songzhu Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in manycore CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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EmSBoT: A lightweight modular software framework for networked robotic systems

EmSBoT: A lightweight modular software framework for network...

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Long Peng Fei Guan Luc Perneel Hasan Fayyad-Kazan Martin Timmerman Department of Electronics and Informatics Vrije Universiteit Brussel (VUB) Brussel-Belgium National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467385244

Developing applications for modern complex networked robotic systems is more challenging due to the introduction of possibly sophisticated communication and coordination aspects. In this paper, we propose EmSBoT, a lightweight embedded component-based software framework targeting resource-constrained networked robotic systems. EmSBoT provides a unified Application Program Interface (API) that hides the heterogeneous distributed environment from applications. Its OS abstraction layer endows it with OS independence and portability. A port-based communication mechanism is adopted to exchange message between loosely coupled components, making the system with fault-tolerance capability. By isolating the communication channels as separate agents, the framework provides uniform and transparent message-passing for agents over node boundaries. We describe the architecture, programming model and core features of EmSBoT in this paper, together with the performance evaluation and behavior validation to demonstrate its efficiency and feasibility.

关键词： Ports (Computers) Robot kinematics Real-time systems Programming Operating systems

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A fast progressive local learning regression ensemble of generalized regression neural networks 15

A fast progressive local learning regression ensemble of gen...

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19th Panhellenic Conference on informatics, PCI 2015

作者： Kokkinos, Yiannis Margaritis, Konstantinos G. Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia str. Thessaloniki54006 Greece

ISBN: (纸本)9781450335515

The Generalized Regression Neural Network (GRNN) is a memory-based supervised learning neural network that performs non linear regressions and output estimation. However, if the number of the hidden layer neurons grows large, the evaluation of an unknown sample has a substantial computational cost. Whereas the GRNN run time can be reduced by parallelism, the computational load can be decreased by neuron reduction that compresses pattern neurons into fewer kernels. While such global models have been studied for a long time, there is another solution;that of local learning algorithms which use neighbourhoods to learn local parameters and create on the fly a local model specifically designed for any particular testing point. For this purpose we create a Progressive Local Learning Ensemble of many local GRNN models. Optimizing the number of k nearest neighbor neurons the method reduces substantially the cost of training as well as of predicting an unknown sample. © 2015 ACM.

关键词： Neural networks

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Enabling Tissue-Scale Cardiac Simulations Using Heterogeneous Computing on Tianhe-2

Enabling Tissue-Scale Cardiac Simulations Using Heterogeneou...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Johannes Langguth Qiang Lan Namit Gaur Xing Cai Mei Wen Chun-Yuan Zhang Simula Research Laboratory Lysaker Norway College of Computer National University of Defense Technology Changsha China National Key Laboratory of Parallel and Distributed Processing Changsha China Department of Informatics University of Oslo Oslo Norway

ISBN: (纸本)9781509053827

We develop a simulator for 3D tissue of the human cardiac ventricle with a physiologically realistic cell model and deploy it on the supercomputer Tianhe-2. In order to attain the full performance of the heterogeneous CPU-Xeon Phi design, we use carefully optimized codes for both devices and combine them to obtain suitable load balancing. Using a large number of nodes, we are able to perform tissue-scale simulations of the electrical activity and calcium handling in millions of cells, at a level of detail that tracks the states of trillions of ryanodine receptors. We can thus simulate arrythmogenic spiral waves and other complex arrhythmogenic patterns which arise from calcium handling deficiencies in human cardiac ventricle tissue. Due to extensive code tuning and parallelization via OpenMP, MPI, and SCIF/COI, large scale simulations of 10 heartbeats can be performed in a matter of hours. Test results indicate excellent scalability, thus paving the way for detailed whole-heart simulations in future generations of leadership class supercomputers.

关键词： Calcium Computational modeling Mathematical model Performance evaluation Hardware Instruction sets Supercomputers

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Multithreaded local learning regularization neural networks for regression tasks 16th

Multithreaded local learning regularization neural networks ...

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16th International Conference on Engineering Applications of Neural Networks, EANN 2015

作者： Kokkinos, Yiannis Margaritis, Konstantinos G. Parallel and Distributed Processing Laboratory Department of Applied Informatics University of Macedonia 156 Egnatia Str. P. O. Box 1591 Thessaloniki54006 Greece

ISBN: (纸本)9783319239811

We explore four local learning versions of regularization networks. While global learning algorithms create a global model for all testing points, the local learning algorithms use neighborhoods to learn local parameters and create on the fly a local model specifically designed for any particular testing point. This approach delivers breakthrough performance in many application domains. Usually however the computational overhead is substantial, and in some cases prohibited. For speeding up the online predictions we exploit both multithreaded parallel implementations as well as interplay between locally optimized parameters and globally optimized parameters. The multithreaded local learning regularization neural networks are implemented with OpenMP. The accuracy of the algorithms is tested against several benchmark datasets. The parallel efficiency and speedup is evaluated on a multi-core system. © Springer International Publishing Switzerland 2015.

关键词： Learning algorithms

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Breaking Ties of Plurality Voting in Ensembles of distributed Neural Network Classifiers Using Soft Max Accumulations

IFIP Advances in Information and Communication Technology

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IFIP Advances in Information and Communication Technology 2014年 436卷 20-28页

An ensemble of distributed neural network classifiers is composed when several different individual neural networks are trained based on their local training data. These classifiers can provide either a single class label prediction, or the normalized via the soft max real value class-outputs that represent posterior probabilities which give the confidence levels. To form the ensemble decision the individual classifier decisions can be combined via the well known majority (or plurality) voting that sums the votes for each class and selects the class that receives most of the votes. While the majority voting is the most popular combination rule many ties in votes can occur, especially in multi-class problems. Ties are usually broken either randomly where the unknown instance is assigned randomly to one of the tied classes or using the class proportions where the tied class with the largest proportion wins. We present a tie breaking strategy that uses soft max confidence accumulations. Every class accumulates a vote and a confidence for this vote. If a tie occurs then the tied class with the maximum confidence sum wins. The proposed tie breaking in the voting process performs very well in all cases of different data distributions on various benchmark datasets. © IFIP International Federation for Information processing 2014.

关键词： distributed computer systems

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parallel and local learning for fast Probabilistic Neural Networks in scalable data mining 13

Parallel and local learning for fast Probabilistic Neural Ne...

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6th Balkan Conference in informatics, BCI 2013

ISBN: (纸本)9781450318518

Probabilistic Neural Networks (PNN) represent knowledge in the form of simple well understood Bayesian models, which are most suitable for data mining applications that also need confidence levels. However if the number N of pattern neurons grows large, the evaluation of a single unknown sample has still O(N) complexity, which is not scale well. parallelism can efficiently work towards speeding up the large neural networks. Whereas the PNN run time can be reduced by parallelism, its computational cost can be decreased by keeping only the globally most important pattern neurons. While such parsimonious global models have been studied for a long time, there is another way that is related to local learning algorithms. Maintain all N pattern neurons but to classify any unknown x use only the local ones, the k nearest neighbor to x neurons. This local learning PNN is investigated here. By using confidence ratio outputs we optimize the number of k nearest neighbor neurons for the best PNN performance. Combined with the parallel approach using p processors the method can reduce the cost of classifying an unknown x from O(N) to O(k/p). Copyright 2013 ACM.

关键词： Neural networks

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Generalized matrix multiplication and its object oriented model

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Scalable Computing 2014年第2期15卷 187-200页

作者： Ganzha, Maria Paprzycki, Marcin Sedukhin, Stanislav G. Systems Research Institute Polish Academy of Sciences Warsaw and Institute of Informatics University of Gdańsk Gdańsk Poland Systems Research Institute Polish Academy of Sciences Warsaw and Department of Management and Technical Sciences Warsaw Management Academy Warsaw Poland Distributed Parallel Processing Laboratory The University of Aizu Aizuwakamatsu City Fukushima 965-8580 Japan

Since the beginning of the 21st century, we observe rapid changes in the area of, broadly understood, computational sciences. One of interesting effects of these changes is the need for reevaluation of the role of dense matrix multiplication. The aim of this paper is two-fold. First, to summarize developments that point toward a need for reconsidering usefulness of matrix multiplication generalized on the basis of the theory of algebraic semirings. Second, to propose generalized matrix-matrix multiply-and-update (MMU) operation and its object oriented model. © 2014 SCPE.

关键词： Matrix algebra

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