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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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Improving vertex-frontier based GPU breadth-first search

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Journal of Central South University 2014年第10期21卷 3828-3836页

作者：杨博卢凯高颖慧徐凯王小平程志权 Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Computer National University of Defense Technology Department of Electronic Science and Engineering National University of Defense Technology Avatar Science Company

Breadth-first search(BFS) is an important kernel for graph traversal and has been used by many graph processing applications. Extensive studies have been devoted in boosting the performance of BFS. As the most effective solution, GPU-acceleration achieves the state-of-the-art result of 3.3×109 traversed edges per second on a NVIDIA Tesla C2050 GPU. A novel vertex frontier based GPU BFS algorithm is proposed, and its main features are three-fold. Firstly, to obtain a better workload balance for irregular graphs, a virtual-queue task decomposition and mapping strategy is introduced for vertex frontier expanding. Secondly, a global deduplicate detection scheme is proposed to remove reduplicative vertices from vertex frontier effectively. Finally, a GPU-based bottom-up BFS approach is employed to process large frontier. The experimental results demonstrate that the algorithm can achieve 10% improvement over the state-of-the-art method on diverse graphs. Especially, it exhibits 2-3 times speedup on low-diameter and scale-free graphs over the state-of-the-art on a NVIDIA Tesla K20 c GPU, reaching a peak traversal rate of 11.2×109 edges/s.

关键词： breadth-first search GPU graph traversal vertex frontier

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A distributed Computing Framework Based on Lightweight Variance Reduction Method to Accelerate Machine Learning Training on Blockchain

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China Communications 2020年第9期17卷 77-89页

作者： Zhen Huang Feng Liu Mingxing Tang Jinyan Qiu Yuxing Peng Science and Technology on Parallel and Distributed Laboratory National University of Defense TechnologyChangsha 410000China H.R.Support Center PLABeijing 100000China

To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution ***,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory *** overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training *** validate the claims,we have conducted several experiments on multiple classical *** show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.

关键词： machine learning optimization algorithm blockchain distributed computing variance reduction

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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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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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A control system architecture with cloud platform for multi-UAV surveillance 4

A control system architecture with cloud platform for multi-...

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4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018

作者： Hong, Chen Shi, Dianxi Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781538693803

Unmanned aerial vehicle is considered one of the most promising technologies. In the multi-UAV systems, a great challenge is to control multiple UAVs remotely by one person without being limited by distance. In this paper, to address this problem, we propose a control system architecture based on cloud platform which has been developed in order to allow multiple users to control and monitor multiple UAVs simultaneously irrespective of the location. Besides, the cloud platform acts as a remote brain for UAVs by providing extra storage and computing resources. This system architecture not only simplifies the development of control station, but also enhances the capabilities of unmanned system. © 2018 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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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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Searching Latent Sub-Goals in Hierarchical Reinforcement Learning as Riemannian Manifold Optimization

Searching Latent Sub-Goals in Hierarchical Reinforcement Lea...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Liu, Sidun Qiao, Peng Dou, Yong Jin, Ruochun National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Hunan China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Hierarchical Reinforcement Learning (HRL) is promising to tackle the long-term sparse reward problem. However, goal conditioned HRL, which decomposes the goal into a series of sub-goals, suffers from sub-goal search inefficiency problems when the observation space is too large. This problem is more severe in a visual observation space, since its high latent dimensions, where the complete dynamics information is preserved, exponentially increase the difficulty of sub-goal search. In view of this, we propose to treat the latent space as a manifold, i.e., a Riemannian manifold. Assisted by the Riemannian manifold optimization, sub-goals can be efficiently searched in the higher-dimensional latent space, with the help of preserving the dynamics information efficiently. Experiments on a series of MuJoCo tasks with visual observation show that the proposed Riemannian manifold optimization, compared with the baseline that directly searches for sub-goals in bounded latent space, improves the success rate by 1.5 times on average. In much higher dimensions where the baseline no longer converges, the success rate of the proposed method is maintained. © 2022 IEEE.

关键词： Reinforcement learning

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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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