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Detailed and clock-driven simulation for HPC interconnection network

Frontiers of Computer science 2016年第5期10卷 797-811页

作者： Wenhao ZHOU Juan CHEN Chen CUI Qian WANG Dezun DONG Yuhua TANG State Key Laboratory of High Performance Computing School of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China

Performance and energy consumption of high performance computing （HPC） interconnection networks have a great significance in the whole supercomputer, and building up HPC interconnection network simulation plat- form is very important for the research on HPC software and hardware technologies. To effectively evaluate the per- formance and energy consumption of HPC interconnection networks, this article designs and implements a detailed and clock-driven HPC interconnection network simulation plat- form, called HPC-NetSim. HPC-NetSim uses application- driven workloads and inherits the characteristics of the de- tailed and flexible cycle-accurate network simulator. Besides, it offers a large set of configurable network parameters in terms of topology and routing, and supports router＇s on/off states. We compare the simulated execution time with the real execution time of Tianhe-2 subsystem and the mean error is only 2.7%. In addition, we simulate the network behaviors with different network structures and low-power modes. The results are also consistent with the theoretical analyses.

关键词： high performance computing clock-driven sim-ulation interconnection network BookSim

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A survey of script learning

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Frontiers of Information technology & Electronic Engineering 2021年第3期22卷 341-373页

作者： Yi HAN Linbo QIAO Jianming ZHENG Hefeng WU Dongsheng LI Xiangke LIAO Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410000China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410000China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China

Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible *** learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw ***,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script *** research field contains three main topics:event representations,script learning models,and evaluation *** each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative *** also discuss the current state of the research and possible future directions.

关键词： Script learning Natural language processing Commonsense knowledge modeling Event reasoning

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Iaso： an autonomous fault-tolerant management system for supercomputers

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Frontiers of Computer science 2014年第3期8卷 378-390页

作者： Kai LU Xiaoping WANG Gen LI Ruibo WANG Wanqing CHI Yongpeng LIU Hongwei TANG Hua FENG Yinghui GAO Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China ATR Laboratory National University of Defense Technology Changsha 410073 China

With the increase of system scale, the inherent reliability of supercomputers becomes lower and lower. The cost of fault handling and task recovery increases so rapidly that the reliability issue will soon harm the usability of supercomputers. This issue is referred to as the ＂reliability wall＂, which is regarded as a critical problem for current and future supercomputers. To address this problem, we propose an autonomous fault-tolerant system, named Iaso, in MilkyWay- 2 system. Iaso introduces the concept of autonomous management in supercomputers. By autonomous management, the computer itself, rather than manpower, takes charge of the fault management work. Iaso automatically manage the whole lifecycle of faults, including fault detection, fault diagnosis, fault isolation, and task recovery. Iaso endows the autonomous features with MilkyWay-2 system, such as self-awareness, self-diagnosis, self-healing, and self-protection. With the help of Iaso, the cost of fault handling in supercomputers reduces from several hours to a few seconds. Iaso greatly improves the usability and reliability of MilkyWay-2 system.

关键词： supercomputer autonomous management fault tolerant fault management MilkyWay-2 system

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Fast image matching algorithm based on affine invariants

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Journal of Central South University 2014年第5期21卷 1907-1918页

作者：张毅卢凯高颖慧 National Laboratory for Parallel and Distributed Processing(National University of Defense Technology) College of Computer National University of Defense Technology College of Electronic Science and Engineering National University of Defense Technology

Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of extended centroid （EC） to build affine invariants. Based on at-fine invariants of the length ratio of two parallel line segments, FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features, and increases the feature diversity of different targets, thus reducing misjudgment rate during recognizing targets. However, it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation. An advanced FIMA is designed to cope with these problems. Experiments prove that the proposed algorithms have better robustness for Gaussian noise, gray-scale change, contrast change, illumination and small three-dimensional rotation. Compared with the latest fast image matching algorithms based on geometry features, FIMA reaches the speedup of approximate 1.75 times. Thus, FIMA would be more suitable for actual ATR applications.

关键词： affine invariants image matching extended centroid robustness performance

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Optimizing guest swapping using elastic and transparent memory provisioning on virtualization platform

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Frontiers of Computer science 2016年第5期10卷 908-924页

作者： Xi LI Pengfei ZHANG Rui CHU Huaimin WANG School of Information Science and Engineering Central South University Changsha 410083 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410008 China

On virtualization platforms, peak memory de- mand caused by hotspot applications often triggers page swapping in guest OS, causing performance degradation in- side and outside of this virtual machine （VM）. Even though host holds sufficient memory pages, guest OS is unable to utilize free pages in host directly due to the semantic gap between virtual machine monitor （MM） and guest operat- ing system （OS）. Our work aims at utilizing the free memory scattered in multiple hosts in a virtualization environment to improve the performance of guest swapping in a transparent and implicit way. Based on the insightful analysis of behav- ioral characteristics of guest swapping, we design and im- plement a distributed and scalable framework HybridSwap. It dynamically constructs virtual swap pools using various policies, and builds up a synthetic swapping mechanism in a peer-to-peer way, which can adaptively choose different vir- tual swap pools. We implement the prototype of HybridSwap and evaluate it with some benchmarks in different scenar- ios. The evaluation results demonstrate that our solution has the ability to promote the guest swapping efficiency indeed and shows a double performance promotion in some cases. Even in the worst case, the system overhead brought by Hy- bridSwap is acceptable.

关键词： virtualization memory management guestswapping performance degradation

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Factorization Machine-based Unsupervised Model Selection Method

Factorization Machine-based Unsupervised Model Selection Met...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Ruyi Wang, Yijie Xu, Hongzuo Zhou, Haifang National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Machine learning is broadly used in many intelligent cybernetic systems. With the burgeoning of the communities of AI, the number of machine learning-based models is rapidly increasing, but picking a suitable and optimal (or relatively good) model from overwhelming options has become a conundrum when deploying a new system. Therefore, we are motivated by an intriguing question: Can we automatically select a proper model for new data? However, unsupervised model selection poses two main challenges: (i) Evaluation and comparison of candidate models on the new data are infeasible due to the lack of labels;and (ii) It is non-trivial to build relationships between model performance and data characteristics when the interaction between these characteristics should be considered. In light of these limitations, this paper proposes a factorization machine-based unsupervised model selection method. Following mainstream model selection protocols, we also leverage model performance on prior known datasets. Differently, we learn higher-order complex relationships between model performance and dataset characteristics. Specifically, our method transfers the historical performance into a second-order function of meta-features and embedding weights by harnessing the power of factorization machine. This function can be subsequently used to select a proper model when given a new dataset. Extensive experiments show that our method obtains more superior model selection performance than five state-of-the-art approaches, and our method executes faster than its competitors by approximate three magnitudes. © 2022 IEEE.

关键词： Factorization

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PartialRC: A Partial Recomputing Method for Efficient Fault Recovery on GPGPUs

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Journal of Computer science & technology 2012年第2期27卷 240-255页

作者：徐新海杨学军薛京灵林宇斐林一松 National Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology Programming Languages and Compilers Group School of Computer Science and Engineering University of New South Wales

GPGPUs are increasingly being used to as performance accelerators for HPC （High Performance Computing） applications in CPU/GPU heterogeneous computing systems, including TianHe-1A, the world＇s fastest supercomputer in the TOP500 list, built at NUDT （National University of Defense technology） last year. However, despite their performance advantages, GPGPUs do not provide built-in fault-tolerant mechanisms to offer reliability guarantees required by many HPC applications. By analyzing the SIMT （single-instruction, multiple-thread） characteristics of programs running on GPGPUs, we have developed PartialRC, a new checkpoint-based compiler-directed partial recomputing method, for achieving efficient fault recovery by leveraging the phenomenal computing power of GPGPUs. In this paper, we introduce our PartialRC method that recovers from errors detected in a code region by partially re-computing the region, describe a checkpoint-based faulttolerance framework developed on PartialRC, and discuss an implementation on the CUDA platform. Validation using a range of representative CUDA programs on NVIDIA GPGPUs against FullRC （a traditional full-recomputing Checkpoint-Rollback-Restart fault recovery method for CPUs） shows that PartialRC reduces significantly the fault recovery overheads incurred by FullRC, by 73.5% when errors occur earlier during execution and 74.6% when errors occur later on average. In addition, PartialRC also reduces error detection overheads incurred by FullRC during fault recovery while incurring negligible performance overheads when no fault happens.

关键词： GPGPU partial recomputing fault tolerance CUDA checkpointing

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Transfer learning for deep neural network-based partial differential equations solving

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Advances in Aerodynamics 2021年第1期3卷 635-648页

作者： Xinhai Chen Chunye Gong Qian Wan Liang Deng Yunbo Wan Yang Liu Bo Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangshaChina China Aerodynamics Research and Development Center MianyangChina.

Deep neural networks(DNNs)have recently shown great potential in solving partial differential equations(PDEs).The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related ***,learning DNNs usually involves tedious training iterations to converge and requires a very large number of training data,which hinders the application of these models to complex physical *** address this problem,we propose to apply the transfer learning approach to DNN-based PDE solving *** our work,we create pairs of transfer experiments on Helmholtz and Navier-Stokes equations by constructing subtasks with different source terms and Reynolds *** also conduct a series of experiments to investigate the degree of generality of the features between different *** results demonstrate that despite differences in underlying PDE systems,the transfer methodology can lead to a significant improvement in the accuracy of the predicted solutions and achieve a maximum performance boost of 97.3%on widely used surrogate models.

关键词： Deep neural network Partial differential equation Surrogate model Transfer learning

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Parallelization of Fast Monte Carlo Dose Calculation for Radiotherapy Treatment Planning on the ARMv8 Architecture 11

Parallelization of Fast Monte Carlo Dose Calculation for Rad...

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11th International Conference on Information science and technology, ICIST 2021

作者： Jin, Chi Wang, Qinglin Zhao, Yang Dou, Yong National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha China

ISBN: (纸本)9781665412667

Monte Carlo (MC) simulation plays a key role in radiotherapy. Since the simulation time of the MC program cannot fully meet the clinical requirements, we use the ARM-based FT-2000+ multi-core processor for parallelization, which provides an effective solution for accelerating MC dose calculation. In this paper, we implement and verify FT-DPM, which is an OpenMP-based MC Dose Planning Method on FT-2000+. FT-DPM utilizes the parallelism of MC simulation and the advantage of ARM architecture to achieve the parallelization on ARM architecture. Meanwhile, we optimize the original DPM program in terms of memory allocation, data structure and data type. The experiments show that, compared with the original DPM code, FT-DPM obtains very accurate results and reaches the maximum speedups of 155.94 times for the electron case. The parallel program based on FT-2000+ only takes 44.1 seconds to simulate the particle transport of 100 million times, showing good clinical application potential. In addition, the speedup and efficiency of FT-DPM running on different core counts are also discussed. © 2021 IEEE.

关键词： Application programs

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Performance Optimization of a CFD Application on Intel Multicore and Manycore Architectures

Performance Optimization of a CFD Application on Intel Multi...

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10th Annual Conference of Advanced Computer Architecture, ACA 2014

作者： Che, Yonggang Zhang, Lilun Wang, Yongxian Xu, Chuanfu Liu, Wei Cheng, Xinghua Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9783662444900

This paper reports our experience optimizing the performance of a high-order and high accurate Computational Fluid Dynamics (CFD) application (HOSTA) on the state of art multicore processor and the emerging Intel Many Integrated Core (MIC) coprocessor. We focus on effective loop vectorization and memory access optimization. A series techniques, including data structure transformations, procedure inlining, compiler SIMDization, OpenMP loop collapsing, and the use of Huge Pages, are explored. Detailed execution time and event counts from Performance Monitoring Units are measured. The results show that our optimizations have improved the performance of HOSTA by 1.61× on a two Intel Sandy Bridge processors based computer node and 1.97× on a Intel Knights Corner coprocessor, the public MIC product. The microarchitecture level effects of these optimizations are also discussed. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Computational fluid dynamics

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