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Fast and efficient parallel breadth-first search with power-law graph transformation

Frontiers of computer Science 2022年第5期16卷 225-227页

作者： Zite JIANG Tao LIU Shuai ZHANG Mengting YUAN Haihang YOU School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Comptuer Science Wuhan UniversityWuhan 430072China

1 Introduction Most real-world graphs are large-scale but unstructured and *** of the most notable characteristics of real-world graphs is the skewed power law degree distribution[1]:most vertices have a few neighbors while a few own a large number of *** characteristics present challenges for efficient parallel graph processing,such as load imbalance,poor locality,and redundant *** from modifying the graph programming abstraction or changing the execution models on different architectures,reducing the irregularity of graph data also improves the performance of graph processing[2].For example,it is wellknown that BFS has a bad temporal locality,but it is possible to transform irregular graphs to more regular ones to improve spatial locality and gain more performance.

关键词： neighbor programming redundant

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TransGPerf:Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance

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Journal of computer Science & Technology 2021年第4期36卷 778-791页

作者： Songjie Niu Shimin Chen State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China

It is challenging to model the performance of distributed graph *** formulation cannot easily capture the diversified factors and complex interactions in the *** learning methods require a large number of training samples to generate an accurate prediction ***,it is time-consuming to run the required graph computation tests to obtain the training *** this paper,we propose TransGPerf,a transfer learning based solution that can exploit prior knowledge from a source scenario and utilize a manageable amount of training data for modeling the performance of a target graph computation *** results show that our proposed method is capable of generating accurate models for a wide range of graph computation tasks on PowerGraph and *** outperforms transfer learning methods proposed for other applications in the literature.

关键词： performance modeling distributed graph computation deep learning transfer learning

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Energy-Constrained Task Scheduling in Heterogeneous Distributed Systems 26

Energy-Constrained Task Scheduling in Heterogeneous Distribu...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Chen Zhu, Jie Huang, Haiping Gao, Yingmeng Nanjing University of Posts & Telecommunications Nanjing210003 China Institute of Computing Technology Academy of Sciences State Key Laboratory Chinese of Computer Architecture Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9798350331684

The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient. © 2023 IEEE.

关键词： Energy utilization

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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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Acorns: A Framework for Accelerating Deep Neural Networks with Input Sparsity 19

Acorns: A Framework for Accelerating Deep Neural Networks wi...

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Proceedings of the International Conference on Parallel architectures and Compilation Techniques

作者： Xiao Dong Lei Liu Peng Zhao Guangli Li Jiansong Li Xueying Wang Xiaobing Feng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728136134

Deep neural networks have been employed in a broad range of applications, including face detection, natural language processing, and autonomous driving. Yet, the neural networks with the capability to tackle real-world problems are intrinsically expensive in computation, hindering the usage of these models. Sparsity in the input data of neural networks provides an optimizing opportunity. However, harnessing the potential performance improvement on modern CPU faces challenges raised by sparse computations of the neural network, such as cache-unfriendly memory accesses and efficient sparse kernel *** this paper, we propose Acorns, a framework to accelerate deep neural networks with input sparsity. In Acorns, sparse input data is organized into our designed sparse data layout, which allows memory-friendly access for kernels in neural networks and opens the door for many performance-critical optimizations. Upon that, Acorns generates efficient sparse kernels for operators in neural networks from kernel templates, which combine directions that express specific optimizing transformations to be performed, and straightforward code that describes the *** evaluations demonstrate Acorns can outperform state-of-the-art baselines by significant speedups. On the real-world detection task in autonomous driving, Acorns demonstrates 1.8-22.6× performance improvement over baselines. Specifically, the generated programs achieve 1.8-2.4× speedups over Intel MKL-DNN, 3.0-8.8× speedups over TensorFlow, and 11.1-13.2× speedups over Intel MKL-Sparse.

关键词： Compiler

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Supply and Demand Relationship of Fresh Retail Based on Accessibility: A Case Study of Guangzhou

Supply and Demand Relationship of Fresh Retail Based on Acce...

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2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Jiang, Rui Hu, Zilin Wei, Zongcai Wu, Kaixin Liang, Jing Liu, Ziye School of Architecture South China Univ. of Technology Guangzhou China State Key Laboratory of Subtropical Building and Urban Science South China Univ.of Technology Guangzhou China School of Computer Science and Engineering South China Univ. of Technology Guangzhou China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Whether the supply-demand relationship of fresh food retail services matches is of great significance to urban planning and construction and residents' lives. This work takes four central districts in Guangzhou as an example to calculate the accessibility of fresh retail in different types of residential areas. In addition, this work explores the spatial clustering distribution relationship of fresh retail supply and demand. Results show that the frequency of purchasing fresh food in central areas is typically higher than that in border areas. However, the supply-demand matching is poor due to the large population size. The online accessibility of newly built urban areas, such as Tianhe District, is better, but offline fresh food retailing dominates. Offline fresh food retail supply and demand matching is better than online in the marginal area. The analysis of the accessibility of fresh food in residential areas is beneficial for providing a reference for urban spatial layout and planning and construction. © ASCE.

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Fresh Retail Layout and Forecast Based on Multi-Source Big Data and Machine Learning: A Case Study of Guangzhou

Fresh Retail Layout and Forecast Based on Multi-Source Big D...

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2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Wu, Kaixin Liu, Ziye Wei, Zongcai Liang, Jing Jiang, Rui Hu, Zilin School of Architecture South China Univ. of Technology Guangzhou China School of Computer Science and Engineering South China Univ. of Technology Guangzhou China State Key Laboratory of Subtropical Building and Urban Science South China Univ. of Technology Guangzhou China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Fresh retail is an important facility in the community life circle. Exploring its spatial distribution is essential for improving the allocation of life service resources. This paper introduces a method based on supply and demand matching and machine learning, designed to analyze and forecast the current state of supply and demand in offline fresh retail. Specifically, we take the four central districts of Guangzhou as the research object and apply the two-step mobile search method to filter the training set grid. Utilizing machine learning, we obtained the feature matrices of these grids and applied them to the supply prediction of the remaining grids. The results indicate that the Commercial Residential Density, Travel Convenience Index, and Food Service Density are the main factors affecting the location selection of offline fresh retail. The supply and demand in Yuexiu and Haizhu Districts show imbalance, and the construction of offline fresh retail facilities must be fortified. © ASCE.

关键词： Adversarial machine learning

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Tetris:A Heuristic Static Memory Management Framework for Uniform Memory Multicore Neural Network Accelerators

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Journal of computer Science & Technology 2022年第6期37卷 1255-1270页

作者： Xiao-Bing Chen Hao Qi Shao-Hui Peng Yi-Min Zhuang Tian Zhi Yun-Ji Chen Distinguished Member,CCF State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Technology University of Science and Technology of ChinaHeFei230026China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai200031China 不详

Uniform memory multicore neural network accelerators(UNNAs)furnish huge computing power to emerging neural network ***,with neural network architectures going deeper and wider,the limited memory capacity has become a constraint to deploy models on UNNA *** how to efficiently manage memory space and how to reduce workload footprints are urgently *** this paper,we propose Tetris:a heuristic static memory management framework for UNNA *** reconstructs execution flows and synchronization relationships among cores to analyze each tensor’s liveness *** the memory management problem is converted to a sequence permutation *** uses a genetic algorithm to explore the permutation space to optimize the memory management strategy and reduce memory *** evaluate several typical neural networks and the experimental results demonstrate that Tetris outperforms the state-of-the-art memory allocation methods,and achieves an average memory reduction ratio of 91.9%and 87.9%for a quad-core and a 16-core Cambricon-X platform,respectively.

关键词： multicore neural network accelerators liveness analysis static memory management memory reuse genetic algorithm

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HTDet:A Clustering Method Using Information Entropy for Hardware Trojan Detection

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Tsinghua Science and Technology 2021年第1期26卷 48-61页

作者： Renjie Lu Haihua Shen Zhihua Feng Huawei Li Wei Zhao Xiaowei Li the University of Chinese Academy of Sciences Beijing 101408China Beijing Institute of Computer Technology and Application Beijing 100854China the State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential *** this paper,we propose HTDet,a novel HT detection method using information entropybased *** maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the *** implies that the regions with the low transitions will provide much more abundant and more important information for HT *** HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under *** DBSCAN is an unsupervised learning algorithm,that can improve the applicability of *** addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan *** on circuit benchmarks demonstrate the effectiveness of HTDet.

关键词： Hardware Trojan(HT)detection information entropy Density-Based Spatial Clustering of Applications with Noise(DBSCAN) unsupervised learning clustering mutual information test patterns generation

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SEER: A Time Prediction Model for CNNs from GPU Kernel's View 21

SEER: A Time Prediction Model for CNNs from GPU Kernel's Vie...

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Proceedings of the 30th International Conference on Parallel architectures and Compilation Techniques

作者： Guodong Liu Sa Wang Yungang Bao State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China and University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China and Institute of Computing Technology (Nanjing) Chinese Academy of Sciences Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China and Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781665442787

With the deepening of research and increasing size of data sets, deep neural networks have become larger and larger. To reduce the training time of large neural networks, researchers propose to optimize neural networks from different levels. When performing optimizations, prior knowledge about execution time of each part of the network can help avoid repeatedly time-consuming testing and profiling process. However it is quite challenging to build an accurate iteration time prediction model, due to opaque underlying implementation of network operators and complex architecture of accelerators. In this paper, we propose SEER, an iteration time prediction model for CNNs, targeting on GPU platforms. We propose to categorize convolution kernels into three different types: Compute-bound, DRAM-bound and Under-utilized, then we build performance model for each type respectively. We combined analytical models and learning-based models to make the performance model accurate and in line with GPU execution model. Experimental results show that, our model achieves 14.71% prediction error on convolution kernels and up to 1.79% prediction error for the overall computation time in one iteration of common CNNs. Besides, when used for selecting the best convolution algorithm, our model shows 7.14% lower error rate than cuDNN's official algorithm picker.

关键词： GPU

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