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Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey

IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 205-234页

作者： Xin Liu Mingyu Yan Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086 School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049 IEEE Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijing 100084China

Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.

关键词： Efficient training graph convolutional networks(GCNs) graph neural networks(GNNs) sampling method

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge Computing 19

Energy-Efficient and Reliable Federated Learning in Heteroge...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xu, Yan Li, Liying Cong, Peijin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture China

ISBN: (纸本)9798350346558

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge Computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. Then, we design a highly reliable device selection scheme based on devices' reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods. © 2022 IEEE.

关键词： Reliability

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Ad Hoc File Systems for High-Performance Computing

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Journal of computer science & Technology 2020年第1期35卷 4-26页

作者： AndréBrinkmann Kathryn Mohror Weikuan Yu Philip Carns Toni Cortes Scott A.Klasky Alberto Miranda Franz-Josef Pfreundt Robert B.Ross Marc-AndréVef Zentrum für Datenverarbeitung Johannes Gutenberg University MainzMainz 55128Germany Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCA 94550U.S.A Department of Computer Science Florida State UniversityTallahasseeFL 32306U.S.A Mathematics and Computer Science Division Argonne National LaboratoryLemontIL 60439U.S.A Department of Computer Architecture Universitat Politecnica de CatalunyaBarcelona 08034Spain Computer Science and Mathematics Division Oak Ridge National LaboratoryOak RidgeTN 37831U.S.A Computer Science Department Barcelona Supercomputing CenterBarcelona 08034Spain Fraunhofer Institute for Industrial Mathematics ITWM Fraunhofer-Platz 1Kaiserslautern 67663Germany

Storage backends of parallel compute clusters are still based mostly on magnetic disks,while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory(NVRAM)are deployed within compute *** these new storage technologies into scientific workflows is unfortunately today a mostly manual task,and most scientists therefore do not take advantage of the faster storage *** approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes,which serve as temporary storage systems for single applications or longer-running *** paper presents results from the Dagstuhl Seminar 17202"Challenges and Opportunities of User-Level File Systems for HPC"and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage *** discussion includes open research questions,such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file *** presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device *** interfaces and semantics are presented,for example those used by the three ad hoc file systems BeeOND,GekkoFS,and *** presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices.

关键词： parallel architectures distributed file system high-performance computing burst buffer POSIX(portable operating system interface)

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Accelerate Bit Manipulation in XiangShan Processer Using RISC-V B Extension and Instruction Fusion

Accelerate Bit Manipulation in XiangShan Processer Using RIS...

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2022 International Conference on Cloud Computing, Internet of Things, and computer Applications, CICA 2022

作者： Zhang, Fawang Tang, Dan Cai, Ye College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China

ISBN: (纸本)9781510656673

In the field of high-performance computing, some application scenarios make extensive use of bit manipulation. RISC-V foundation issues B extension to reduce the number of instructions during the static compilation. But this extension does not cover the more frequent operations fully. Instruction fusion technology can fuse multiple instructions into one instruction at the decode stage to dynamically reduce the number of instructions. Therefore, this technology can supplement RISC-V B extension uncovered instruction, enhancing the processor’s capabilities for bit manipulation. This paper implements the RISC-V B extension 1.0 version on the Xiangshan processor. For accelerating more bit manipulation, we profiled general benchmarks by the simulator, proposed ten new instructions fusion targets, and implemented a fusion decode unit. Finally, we use Coremark and SPEC CPU 2006 integer to evaluate the performance. The Coremark results show that dynamic instructions reduce by 18.14%, and the performance improves by 12.09%. © 2022 SPIE.

关键词： Decoding

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Modeling the failures of power-aware data centers by leveraging heat recirculation

Modeling the failures of power-aware data centers by leverag...

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作者： Feng, Hao Deng, Yuhui Yu, Liang Department of Computer Science Jinan University Guangzhou China Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow-based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model. © 2019 John Wiley & Sons, Ltd.

关键词： Energy efficiency

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Fast implementation of DGEMM on Fermi GPU

Fast implementation of DGEMM on Fermi GPU

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2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC11

作者： Tan, Guangming Li, Linchuan Triechle, Sean Phillips, Everett Bao, Yungang Sun, Ninghui Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science China Nvidia Corporation China

ISBN: (纸本)9781450307710

In this paper we present a thorough experience on tuning double-precision matrix-matrix multiplication (DGEMM) on the Fermi GPU architecture. We choose an optimal algorithm with blocking in both shared memory and registers to satisfy the constraints of the Fermi memory hierarchy. Our optimization strategy is further guided by a performance modeling based on micro-architecture benchmarks. Our optimizations include software pipelining, use of vector memory operations, and instruction scheduling. Our best CUDA algorithm achieves comparable performance with the latest CUBLAS library1. We further improve upon this with an implementation in the native machine language, leading to 20% increase in performance. That is, the achieved peak performance (efficiency) is improved from 302Gflop/s (58%) to 362Gflop/s (70%). Copyright 2011 ACM.

关键词： Graphics processing unit

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A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

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Journal of computer science & Technology 2020年第1期35卷 209-220页

作者： Sa Wang Yan-Hai Zhu Shan-Pei Chen Tian-Ze Wu Wen-Jie Li Xu-Sheng Zhan Hai-Yang Ding Wei-Song Shi Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518055China Alibaba Inc. Hangzhou 311121China Department of Computer Science Wayne State UniversityMichiganMI 48202U.S.A

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be *** resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable *** of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource *** this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC *** is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural *** experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.

关键词： resource management neural network resource efficiency tail latency

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Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization

Differential Evolution with Self-adaptation and Local Search...

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Congress on Evolutionary Computation

作者： Ales Zamuda Janez Brest Borko Boskovic Viljem Zumer Computer Architecture and Languages Laboratory Institute of Computer Science Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

This paper presents differential evolution with self-adaptation and local search for constrained multiobjective optimization algorithm (DECMOSA-SQP), which uses the self-adaptation mechanism from DEMOwSA algorithm presented at CEC 2007 and a SQP local search. The constrained handling mechanism is also incorporated in the new algorithm. Assessment of the algorithm using CEC 2009 special session and competition on constrained multiobjective optimization test functions is presented. The functions are composed of unconstrained and constrained problems. Their results are assessed using the IGD metric. Based on this metric, algorithm strengths and weaknesses are discussed.

关键词： Constraint optimization Testing Evolutionary computation computer science Quadratic programming Simulated annealing Computational modeling Guidelines computer architecture Search methods

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Optimizing Multi-Dimensional Packet Classification for Multi-Core Systems

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Journal of computer science & Technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

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Cryptanalysis of dragon scheme

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The Journal of China Universities of Posts and Telecommunications 2010年第4期17卷 80-87页

作者： YUAN Feng HU Yu-pu WANG Yan OU Hai-wen Key Laboratory of Computer Networks and Information Security Ministry of Education Xidian University Xi'an 710071 China School of Science Xi'an University of Architecture and Technology Xi'an 710055 China Key Laboratory of Information Security Beijing Electronic Science and Technology Institute Beijing 100070 China

Patarin proposed the dragon scheme, pointed out the insecurity of the dragon algorithm with one hidden monomial and suggested a candidate dragon signature algorithm with a complicated function. This paper presents an algebraic method to attack the candidate dragon signature algorithm. The attack borrows the basic idea of the attack due to Kipnis and Shamir, and utilizes the underlying algebraic structure of the candidate dragon signature algorithm over the extension field to derive a way to enable the variable Y be viewed as a fixed value. The attack recovers the private keys efficiently when the parameters are n≤2s and D=[logq^d]≤3.

关键词： cryptography public key cryptography polynomials mapping multivariate dragon scheme algebraic method cryptanalysis

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