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Early Experiences with Write-Write Design of NFS over RDMA

Early Experiences with Write-Write Design of NFS over RDMA

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International Conference on Networking, architecture, and Storage (NAS)

作者： Bo Li Panyong Zhang Zhigang Huo Dan Meng National Research Center for Intelligent Computing Systems Institute of Computing Technology Key Laboratory of Computer System and Architecture Chinese Academy of Sciences China National Research Center for Intelligent Computing Systems Institute of Computing Technology Key Laboratory of Computer System and Architecture Chinese Academy and Sciences China

The network file system (NFS) protocol, as the de facto standard for sharing files in a distributed environment, has deployed Infiniband as the underlying transport of sunRPC, namely NFS over RDMA. In the current Read-Write design of NFS over RDMA, NFS write performance is limited for not fully utilizing the features of Infiniband. In this paper, we take on the challenge of enhancing the write performance of NFS. We propose and evaluate a new design of sunRPC over RDMA, namely Write-Write design. To guarantee the security of our design, we propose an HCA-based memory protection extension of Infiniband. Evaluations show that our Write-Write design increases the kernel-to-kernel RPC bandwidth by 15~27%. In real disk test, our Write-Write design gains 15%~22% in multi-client benchmarks compared with the Read-Write design.

关键词： Security Protection Bandwidth Scalability computer architecture computer networks Distributed computing File systems Benchmark testing Protocols

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Codelet Scheduling by Genetic Algorithm

Codelet Scheduling by Genetic Algorithm

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Songwen Pei Jinkai Wang Wenyang Cui Linhua Jiang Tongsheng Geng Jean-Luc Gaudiot Stéphane Zuckerman Shanghai Key Lab of Modern Optical Systems University of Shanghai for Science and Technology Shanghai China Parallel Systems and Computer Architecture Lab University of California Irvine CA USA Computer Architecture & Parallel Systems Laboratory University of Delaware Newark DE

ISBN: (纸本)9781509032068

Codelet model is a fine-grained, event-driven hybrid parallel model inspired by dataflow, whose performance depends on the scheduling policy. How to design optimal codelet scheduling policy based on the features of tasks is important to the codelet-based system performance. In this paper, we propose an adaptive codelet scheduling policy by combing "pure" genetic algorithm for tasks with complex dependencies. It is verified that the policy is effective based on bunches of experimental results.

关键词： Computational modeling Schedules Genetic algorithms Scheduling Runtime Optimal scheduling Sociology

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Theoretical treatment of sink scheduling problem in wireless sensor networks

Theoretical treatment of sink scheduling problem in wireless...

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2011 IEEE Conference on computer Communications Workshops, INFOCOM WKSHPS 2011

作者： Gu, Yu Ji, Yusheng Li, Jie Zhao, Baohua Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan State Key Laboratory of Networking and Switching Technology Beijing 100876 China School of Computer Science Univ. of Science and Technology of China Hefei Anhui 230027 China Department of Computer Science University of Tsukuba Ibaraki 305-8573 Japan

ISBN: (纸本)9781457702488

Sink Scheduling, in the form of scheduling multiple sinks among sink sites to leverage traffic burden, is an effective mechanism for the energy-efficiency of wireless sensor networks (WSNs). Due to the inherent difficulty (NP-hard in general), existing works on this topic mainly focus on heuristic/greedy algorithms and theoretic results remain unknown. In this paper, we fill in the research blank with two algorithms. The first one is based on the Column Generation (CG). It decomposes the original problem into two sub problems and solve them iteratively to approach the optimal solution. However, due to its high computational complexity, this algorithm is only suitable for small scale networks. The other one is a polynomial-time algorithm based on relaxation techniques to obtain an upperbound, which can serve as a performance benchmark for other algorithms on this problem. Through comprehensive simulations, we evaluate the efficiency of proposed algorithms. © 2011 IEEE.

关键词： Wireless sensor networks

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Two-Stage Safe Reinforcement Learning for High-Speed Autonomous Racing

Two-Stage Safe Reinforcement Learning for High-Speed Autonom...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jingyu Niu Yu Hu Beibei Jin Yinhe Han Xiaowei Li Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture ICT CAS University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Decision making for autonomous driving is a safety-critical control problem. Prior works of safe reinforcement learning either tackle the problem with reward shaping or with modifying the reinforcement learning exploration process. However, the former cannot guarantee the safety during the learning process, while the latter relies heavily on expertise to design exquisite exploration policy. Currently, only short-term decision makings for low-speed driving were achieved in road scenes with basic geometries. In this paper, we propose a two-stage safe reinforcement learning algorithm to automatically learn a long-term policy for high-speed driving that guarantees safety during the entire training. In the first learning stage, model-free reinforcement learning is followed by a rule-based safeguard module to avoid danger at low speed without expert ne-tuning. In the second learning stage, the rule-based module is replaced with a data-driven counterpart to develop a closed-form analytical safety solution for high-speed driving. Moreover, an adaptive reward function is designed to match the different objectives of the two learning stages for faster convergence to an optimal policy. Experiments are conducted on a racing simulator TORCS which has complex racing tracks (e.g. sharp turns, hills). Compared with the state-of-the-art baselines, the results show that our method achieves zero safety violation and quickly converges to a more efficient and stable policy with an average speed of 127 km/h (3.3% higher than the best result of baselines) and an average swing of 3.96 degrees.

关键词： Training Geometry Roads Decision making Reinforcement learning Safety Cybernetics

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Shape Analysis of Volume Models by Euclidean Distance Transform and Moment Invariants

Shape Analysis of Volume Models by Euclidean Distance Transf...

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10th IEEE International Conference on computer Aided Design and computer Graphics(第十届CAO/Graphics国际会议)

作者： Dong Xu Hua Li Key Laboratory of Intelligent Information Processing Key Laboratory of Computer System and Architecture National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese

In this paper,volume models are obtained from closed surface models by an accurate voxelization method which can handle the hidden cavities. This kind of 3D binary images is then converted to gray-level images by a fast Euclidean distance transform (EDT).Moment invariants (MIs) which are invariant shape descriptors under similarity transformations,are then computed based on the gray images. Applications in shape analysis area such as principal axis determination,skeleton and medial axis extraction,and shape retrieval can be carried out base on EDT and MIs.

关键词： Shape Euclidean distance Skeleton Principal component analysis Laboratories Image converters Data mining Computed tomography Clouds Iterative closest point algorithm

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MemoryIO: An Extended I/O Technology in Embedded systems

MemoryIO: An Extended I/O Technology in Embedded Systems

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International Conference on Networking, architecture, and Storage (NAS)

作者： Xiaojun Yang Tao Liu Fei Chen Hailiang Cheng National Research Center for Intelligent Computing Systems Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences China Graduate University of Advanced Studies (SOKENDAI) Beijing China Key Laboratory of Computer System and Architecture Institute of Computing TechnologyNational Research Center for Intelligent Computing Systems Chinese Academy and Sciences China

MemoryIO, a sort of extended I/O in embedded systems, is presented in this paper. MemoryIO makes it powerful for embedded systems to achieve the high-performance interconnect. In view of the facts that the main memory system is absolutely necessary in any embedded system, and not all embedded systems integrate HyperTransport (HT), PCI Express or RapidIO interface, the MemoryIO based interconnect in embedded systems has more universalities compared with that based on HT, PCI Express or RapidIO. MemoryIO can not only thoroughly compensates for the lack of high performance data transfer channel, but also efficiently utilizes the memory bus bandwidth and the direct memory access (DMA) engine to reduce the latency for data transfer in embedded systems. This paper discusses some key technologies of MemoryIO, and presents its application in DCNet and the implementation of MemoryIO IP core. The MemoryIO technology can be used in various systems, but not limited to embedded systems.

关键词： Embedded system Payloads Switches Clocks Storage area networks Physical layer Protocols

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Design of NIC Based on I/O Processor for Cluster Interconnect Network

Design of NIC Based on I/O Processor for Cluster Interconnec...

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International Conference on Networking, architecture, and Storage (NAS)

作者： Xiaojun Yang Dongdong Wu Ninghui Sun National Research Center for Intelligent Computing Systems Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences Beijing China

An effective interconnect network interface card (NIC) is critical to the achievement of a high-performance cluster system. An original NIC architecture based on the Intel IOP310 I/O processor chipset is presented in this paper. The NIC is a part of DCNet, which is the cluster interconnect network developed by NCIC. The I/O processor makes it powerful for the NIC to offload the processing of communication protocol from the host CPU. In the NIC architecture, the memory bus is extended to be a local bus for system peripheral interconnection, and a memory integrated network interface (MINI) is implemented and embedded. The testing results of DCNet show that the NIC obtains reasonable user-level communication performance, and prove that the NIC architecture, which based on I/O processor and the MINI approach, is feasible and effective to achieve the high performance.

关键词： computer architecture computer networks Network interfaces Supercomputers Bandwidth Intelligent systems Intelligent networks computer interfaces Power system interconnection Protocols

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

引用

29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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A New Algorithm for Non-rigid Point Matching Using Geodesic Graph Model

A New Algorithm for Non-rigid Point Matching Using Geodesic ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Deheng Qian Tianshi Chen Hong Qiao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479970995

Point matching is a problem of finding the optimum matching between two sets of key points which are extracted from the surfaces of objects. A popular approach represents the features of a set of points with a graph model. Traditionally, the measurement applied in the graph model is the Euclidian distance, which is not suitable for objects with non-rigid deformations. In this paper, we propose a novel graph model called the geodesic graph model (GGM) which uses a geodesic-like distance as its measurement. GGM can better tackle non-rigid deformations because the geodesic-like distance is a kind of invariant structural feature during non-rigid deformations. The building process of the GGM is justified under the assumption that all the key points are spanning on a manifold. To further handle the deviations of key point locations, we come up with a feature weighting process to increase our algorithm's robustness. We conduct several experiments on different kinds of deformations over several widely used datasets. Experimental results demonstrate the effectiveness of our algorithm.

关键词： Point matching non-rigid deformation manifold geodesic distance

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TNASP: a transformer-based NAS predictor with a self-evolution framework 21

TNASP: a transformer-based NAS predictor with a self-evoluti...

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Proceedings of the 35th International Conference on Neural Information Processing systems

作者： Shun Lu Jixiang Li Jianchao Tan Sen Yang Ji Liu Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Kuaishou Technology

ISBN: (纸本)9781713845393

Predictor-based Neural architecture Search (NAS) continues to be an important topic because it aims to mitigate the time-consuming search procedure of traditional NAS methods. A promising performance predictor determines the quality of final searched models in predictor-based NAS methods. Most existing predictor-based methodologies train model-based predictors under a proxy dataset setting, which may suffer from the accuracy decline and the generalization problem, mainly due to their poor abilities to represent spatial topology information of the graph structure data. Besides the poor encoding for spatial topology information, these works did not take advantage of the temporal information such as historical evaluations during training. Thus, we propose a Transformer-based NAS performance predictor, associated with a Laplacian matrix based positional encoding strategy, which better represents topology information and achieves better performance than previous state-of-the-art methods on NAS-Bench-101, NAS-Bench-201, and DARTS search space. Furthermore, we also propose a self-evolution framework that can fully utilize temporal information as guidance. This framework iteratively involves previous evaluation information as constraints into current optimization iteration, thus further improving the performance of our predictor. Such framework is model-agnostic, thus can enhance performance on various backbone structures for the prediction task. Our proposed method helped us rank 2nd among all teams in CVPR 2021 NAS Competition Track 2: Performance Prediction Track.

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