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Realization and optimization DGEMM on ARMv8 64-bit multi-core processor

Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 37-43页

作者： Jiang, Hao Wang, Feng Zuo, Ke Li, Kuan Yang, Can-Qun College of Computer Science National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

The double-precision matrix-matrix multiplication (DGEMM) on ARMv8 64-bit multi-core processor architecture was realized and optimized, and the optimal model for the purpose of maximizing the compute-to-memory access ratio was built to design DGEMM kernel. The ARM 64-bit memory accessing instruction, Cache pre-fetching instruction and NEON vector FMA instruction were utilized through instruction reordering and loop unrolling to construct the kernel assembly codes. The blocking and packing algorithms and parallel methods from GotoBLAS (OpenBLAS) were chosen, and the results showed that the floating-point peak efficiency can achieve 82% with one thread and 80% with eight threads, respectively. As the fastest DGEMM implementation on ARMv8 64-bit processor, it improves the peak performance by 8.3% and 16.7% compared to ATLAS. ©, 2014, Northeastern University. All right reserved.

关键词： Digital arithmetic

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Balancing CPU-GPU Collaborative High-Order CFD Simulations on the Tianhe-1A Supercomputer

Balancing CPU-GPU Collaborative High-Order CFD Simulations o...

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International Symposium on parallel and distributed Processing (IPDPS)

作者： Chuanfu Xu Lilun Zhang Xiaogang Deng Jianbin Fang Guangxue Wang Wei Cao Yonggang Che Yongxian Wang Wei Liu College of Computer Science National University of Defense Technology Changsha P.R. China parallel and Distributed Systems Group Delft University of Technology Delft The Netherlands State Key Laboratory of Aerodynamics Mianyang P.R. China National University of Defense Technology Changsha Hunan CN

ISBN: (纸本)9781479938018

HOSTA is an in-house high-order CFD software that can simulate complex flows with complex geometries. Large scale high-order CFD simulations using HOSTA require massive HPC resources, thus motivating us to port it onto modern GPU accelerated supercomputers like Tianhe-1A. To achieve a greater speedup and fully tap the potential of Tianhe-1A, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present multiple novel techniques to balance the loads between the store-poor GPU and the store-rich CPU, and overlap the collaborative computation and communication as far as possible. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per Tianhe-1A node for HOSTA by 2.3X, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 Tianhe-1A nodes. With our method, we have successfully simulated China's large civil airplane configuration C919 containing 150M grid cells. To our best knowledge, this is the first paper that reports a CPUGPU collaborative high-order accurate aerodynamic simulation result with such a complex grid geometry.

关键词： Graphics processing units Kernel Collaboration Computational fluid dynamics Computational modeling Memory management Performance evaluation

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Analysis and evaluation method for linpack benchmark

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Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 102-107页

作者： Du, Yun-Fei Yang, Can-Qun Wang, Feng Yi, Hui-Zhan School of Computer National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

The basic algorithm of HPL was introduced. Two optimization methods of communication, i.e., advanced-lookahead and dynamic broadcasting algorithm, were proposed. The performances of the two optimization methods were evaluated on the Tianhe-2 system, and compared with the performance of traditional HPL. The Linpack evaluating methods were discussed, the choice methods of the key parameters affecting the performance were given, and the effect of the high-speed interconnection network on Linpack performance was analyzed. The results showed that the higher the node performance, the greater the effect of the network bandwidth and latency on the performances of HPL. ©, 2014, Northeastern University. All right reserved.

关键词： Large scale systems

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance Computers

SSRN

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SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

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Correntropy Supervised Non-negative Matrix Factorization

Correntropy Supervised Non-negative Matrix Factorization

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International Joint Conference on Neural Networks

作者： Wenju Zhang Naiyang Guan Dacheng Tao Bin Mao Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan China 410073 Center for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia College of Science National University of Defense Technology Changsha Hunan China 410073 Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan China 410073

ISBN: (纸本)9781479919611

Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.

关键词： NMF subsequent classification NMF protocol matrix decomposition outliers Nonnegative LOSS FUNCTION images REDUCTION METHODS Dataset computer vision

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Maximizing Uniform Multicast Throughput in Multi-Channel Dense Wireless Sensor Networks

Maximizing Uniform Multicast Throughput in Multi-Channel Den...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xianlong Jiao Guirong Chen Xiaodong Wang Yuli Chen Li Yang College of Information System and Management National University of Defense Technology Changsha Information and Navigation College Air Force Engineering University Xi'an Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Chongqing Guanyinqiao Elementary School Chongqing Chongqing Liangjiangxinqu Renhe Experimental School Chongqing

ISBN: (纸本)9781509056972

This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions.

关键词： Throughput Wireless sensor networks Approximation algorithms Schedules Data communication Algorithm design and analysis Relays

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A review on the churn character of P2P networks

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Jisuanji Xuebao/Chinese Journal of Computers 2011年第9期34卷 1563-1577页

作者： Fu, Zhi-Peng Wang, Huai-Min Shi, Dian-Xi Zou, Peng School of Computer Science National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

P2P application is one of the most popular applications in the Internet, but its performance is badly influenced by the churn character-the nodes continuous arrival and departure. On the basis of introducing the origin of the churn, the definition of it and its influences to the P2P system performance, we minutely describe the research on the statistical properties, and discover some dynamic law including nodes' session time generally obey the heavy-tailed distribution. Then we detail the measurement methods. For passive monitoring, active monitoring, sample and so on, we present the advantages and disadvantages of each method, and explain the corresponding improving method to enhance the accuracy of the measurement. After that we elaborate the resilience strategies to the churn. Towards the neighbor selection, failure recovery, replica maintenance, link lifetime maintenance and so on, we present also the advantage and disadvantage of each strategy, and present our view to each of aspects. Finally, the summary and prospect of the future research are given.

关键词： Peer to peer networks

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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

Big data collaborative artificial intelligence and high-perf...

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

作者： Pan Liu Gongjie Liu Jie Liu Chencheng Liu Wenbin Xiong Zhangcun Tang Yinzhao Zhou Yonggang Wang Zhouming Yuan Tianfu Innovation Energy Establishment Chengdu China Beijng PARATERA Technology Corp. Ltd Beijing China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Central South University of Forestry and Technology Changsha China Nuclear and Radiation Safety Center MEE Beijing China University of Electronic Science and Technology of China Chengdu China

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson’s criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach.

关键词：

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引用

International Conference on parallel and distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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