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arXiv 2018年

作者： Ren, Rui Li, Jinheng Wang, Lei Zhan, Jianfeng Cao, Zheng State Key Laboratory of Computer Architecture Institute of Computing Technology CAS University of Chinese Academy of Sciences Sichuan University Alibaba Co. Ltd.

In warehouse-scale cloud datacenters, co-locating online services and offline batch jobs is an efficient approach to improving datacenter utilization. To *** facilitate the understanding of interactions among the co-located workloads and their real-world operational demands, Alibaba recently released a cluster usage and colocated workload dataset, which is the first publicly dataset with precise information about the category of each job. In this paper, we perform a deep analysis on the released Alibaba workload dataset, from the perspective of anomaly analysis and diagnosis. .rough data preprocessing, node similarity analysis based on Dynamic TimeWarping (DTW), co-locatedworkloads characteristics analysis and anomaly analysis based on iForest, we reveals several insights including: (1) .e performance discrepancy of machines in Alibaba's production cluster is relatively large, for the distribution and resource utilization of co-located workloads is not balanced. For instance, the resource utilization (especially memory utilization) of batch jobs is fluctuating and not as stable as that of online containers, and the reason is that online containers are longrunning jobs with more memory-demanding and most batch jobs are short jobs, (2) Based on the distribution of co-located workload instance numbers, the machines can be classified into 8 workload distribution categories1. And most *** of machine resource utilization curves are similar in the same workload distribution category. (3) In addition to the system failures, unreasonable scheduling and workload imbalance are the main causes of anomalies in Alibaba's cluster. Copyright © 2018, The Authors. All rights reserved.

关键词： Containers

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HPC AI500: The Methodology, Tools, Roofline Performance Models, and Metrics for Benchmarking HPC AI systems

arXiv

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arXiv 2020年

作者： Jiang, Zihan Wang, Lei Xiong, Xingwang Gao, Wanling Luo, Chunjie Tang, Fei Lan, Chuanxin Li, Hongxiao Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences BenchCouncil [International Open Benchmarking Council University of Chinese Academy of Sciences

The recent years witness a trend of applying large-scale distributed deep learning algorithms in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality. The HPC community feels a great interest in building the HPC AI systems that are dedicated to running those workloads. The HPC AI benchmarks accelerate the process. Unfortunately, benchmarking HPC AI systems at scale raises serious challenges. None of previous HPC AI benchmarks achieve the goal of being equivalent, relevant, representative, affordable, and *** paper presents a comprehensive methodology, tools, Roofline performance models, and innovative metrics for benchmarking, optimizing, and ranking HPC AI systems, which we call HPC AI500 V2.0. We abstract the HPC AI system into nine independent layers, and present explicit benchmarking rules and procedures to assure equivalence of each layer, repeatability, and replicability. On the basis of AIBench–by far the most comprehensive AI benchmarks suite, we present and build two HPC AI benchmarks from both business and scientific computing: Image Classification, and Extreme Weather Analytics, achieving both representativeness and affordability. To ranking the performance and energy-efficiency of HPC AI systems, we propose Valid FLOPS, and Valid FLOPS per watt, which impose a penalty on failing to achieve the target quality. We propose using convolution and GEMM— the two most intensively-used kernel functions of AIBench to measure the upper bound performance of the HPC AI systems, and present HPC AI roofline models for guiding performance optimizations. The evaluations show our methodology, benchmarks, performance models, and metrics can measure, optimize, and rank the HPC AI systems in a scalable, simple, and affordable way. The specification, source code, datasets, and benchmarking data are publicly available from http://***/benchhub/hpc-ai500-benchmark . Copyright

关键词： Benchmarking

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HyGCN: A GCN accelerator with hybrid architecture

arXiv

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arXiv 2020年

作者： Yan, Mingyu Deng, Lei Hu, Xing Liang, Ling Feng, Yujing Ye, Xiaochun Zhang, Zhimin Fan, Dongrui Xie, Yuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences University of California Santa Barbara United States

Inspired by the great success of neural networks, graph convolutional neural networks (GCNs) are proposed to analyze graph data. GCNs mainly include two phases with distinct execution patterns. The Aggregation phase, behaves as graph processing, showing a dynamic and irregular execution pattern. The Combination phase, acts more like the neural networks, presenting a static and regular execution pattern. The hybrid execution patterns of GCNs require a design that alleviates irregularity and exploits regularity. Moreover, to achieve higher performance and energy efficiency, the design needs to leverage the high intra-vertex parallelism in Aggregation phase, the highly reusable inter-vertex data in Combination phase, and the opportunity to fuse phase-by-phase execution introduced by the new features of GCNs. However, existing architectures fail to address these demands. In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority-based off-chip memory access coordination to improve off-chip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509× speedup with 2500× energy reduction and average 6.5× speedup with 10× energy reduction, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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When Deep Learning Meets the Edge: Auto-Masking Deep Neural Networks for Efficient Machine Learning on Edge Devices

When Deep Learning Meets the Edge: Auto-Masking Deep Neural ...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Ning Lin Hang Lu Xing Hu Jingliang Gao Mingzhe Zhang Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering University of California Santa Barbara

Deep neural network (DNN) has demonstrated promising performance in various machine learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring DNN models directly on edge devices trends the development of intelligent systems, like self-driving cars, smart Internet-of-Things (IoTs) and autonomous robotics. The on-device DNN model is obtained by expensive training via vast volumes of high-quality training data in the cloud datacenter, and then deployed into these devices, expecting it to work effectively at the edge. However, edge device always deals with low-quality images caused by compression or environmental noise pollutions. The well-trained model, though could work perfectly on the cloud, cannot adapt to these edge-specific conditions without remarkable accuracy drop. In this paper, we propose an automated strategy, called "AutoMask", to embrace effective machine learning and accelerate DNN inference on edge devices. AutoMask comprises end-to-end trainable software strategies and cost-effective hardware accelerator architecture to improve the adaptability of the device without compromising the constrained computation and storage resources. Extensive experiments, over ImageNet dataset and various state-of-the-art DNNs, show that AutoMask achieves significant inference acceleration and storage reduction while maintains comparable accuracy level on embedded Xilinx Z7020 FPGA, as well as NVIDIA Jetson TX2.

关键词：

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AutoFlow: Hotspot-aware, dynamic load balancing for distributed stream processing

arXiv

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arXiv 2021年

作者： Lu, Pengqi Yuan, Liang Zhang, Yunquan Cao, Hang Li, Kun State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

Stream applications are widely deployed on the cloud. While modern distributed streaming systems like Flink and Spark Streaming can schedule and execute them efficiently, streaming dataflows are often dynamically changing, which may cause computation imbalance and backpressure. We introduce AutoFlow, an automatic, hotspot-aware dynamic load balance system for streaming dataflows. It incorporates a centralized scheduler which monitors the load balance in the entire dataflow dynamically and implements state migrations correspondingly. The scheduler achieves these two tasks using a simple asynchronous distributed control message mechanism and a hotspot-diminishing algorithm. The timing mechanism supports implicit barriers and a highly efficient state-migration without global barriers or pauses to operators. It also supports a time-window based load-balance measurement and feeds them to the hotspot-diminishing algorithm without user interference. We implemented AutoFlow on top of Ray, an actor-based distributed execution framework. Our evaluation based on various streaming benchmark dataset shows that AutoFlow achieves good loadbalance and incurs a low latency overhead in highly data-skew workload. © 2021, CC BY.

关键词： Distributed parameter control systems

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On iterative liveness-enforcement for a class of generalized Petri nets

On iterative liveness-enforcement for a class of generalized...

引用

2012 IEEE International Conference on Automation Science and Engineering: Green Automation Toward a Sustainable Society, CASE 2012

作者： Hou, Yifan Liu, Ding Zhou, Mengchu School of Electro-Mechanical Engineering Xidian University No. 2 South Taibai Road Xi'an 710071 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781467304283

This work presents an iterative liveness-enforcing method for a class of generalized Petri nets, which can model flexible manufacturing systems. The proposed method checks the liveness of net models using mixed integer programming and controls the token allocations of resource places instead of siphons using a liveness and resource usage ratio-enforcing supervisor. The enumeration of a kind of special structures, which is required in the previous work, is avoided and the number of iterations is bounded by the number of shared resource places in the net model. All strict minimal siphons in the controlled systems are minimally controlled. Several explanatory examples are used to illustrate this method. © 2012 IEEE.

关键词： Petri nets

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Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

引用

Cancer Communications 2018年第1期38卷 50-62页

作者： Jin-Cheng Guo Yang Wu Yang Chen Feng Pan Zhi-Yong Wu Jia-Sheng Zhang Jian-Yi Wu Xiu-E Xu Jian-Mei Zhao En-Min Li Yi Zhao Li-Yan Xu Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes Shantou University Medical CollegeShantouGuangdong 515041P.R.China Department of Biochemistry and Molecular Biology Shantou University Medical CollegeShantouGuangdong 515041P.R.China Departments of Oncology Surgery Shantou Central HospitalAffiliated Shantou Hospital of Sun Yat-Sen UniversityShantouGuangdong 515041P.R.China Institute of Oncologic Pathology Shantou University Medical CollegeShantouGuangdong 515041P.R.China Key Laboratory of Intelligent Information Processing Advanced Computer Research CenterState Key Laboratory of Computer ArchitectureInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China

Background:Esophageal squamous cell carcinoma(ESCC)is the predominant subtype of esophageal carcinoma in *** study was to develop a staging model to predict outcomes of patients with ***:Using Cox regression analysis,principal component analysis(PCA),partitioning clustering,Kaplan-Meier analysis,receiver operating characteristic(ROC)curve analysis,and classification and regression tree(CART)analysis,we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 ***:Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes(PCGs)and 635 long non-coding RNAs(lncRNAs)were associated with the survival of patients with *** categorized these PCGs and lncRNAs into three principal components(PCs),which were used to cluster the patients into three *** analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging(area under ROC curve[AUC]:0.69 vs.0.65,P<0.05).Accord-ingly,we constructed a molecular disaggregated model comprising one lncRNA and two PCGs,which we desig-nated as the LSB staging model using CART analysis in the GSE63624 *** LSB staging model classified the GSE63622 dataset of patients into three different groups,and its effectiveness was validated by analysis of another cohort of 105 ***:The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

关键词： Long non-coding RNA Protein-coding gene Esophageal squamous cell carcinoma Overall survival Staging model Transcriptome

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Off-path leakage power aware routing for SRAM-based FPGAs 12

Off-path leakage power aware routing for SRAM-based FPGAs

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Keheng Huang Yu Hu Xiaowei Li Bo Liu Hongjin Liu Jian Gong Graduate University of Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Control Laboratory Beijing Institute of Control Engineering China

ISBN: (纸本)9783981080186

As the feature size and threshold voltage reduce, leakage power dissipation becomes an important concern in SRAM-based FPGAs. This work focuses on reducing the leakage power in routing resources, and more specifically, the leakage power dissipated in the used part of FPGA device, which is known as the active leakage power. We observe that the leakage power in off-path transistors takes up most of the active leakage power in multiplexers that control routing, and strongly depends on Hamming distance between the state of the on-path input and the states of the off-path inputs. Hence, an off-path leakage power aware routing algorithm is proposed to minimize Hamming distance between the state of on-path input and the states of off-path inputs for each multiplexer. Experimental results on MCNC benchmark circuits show that, compared with the baseline VPR technique, the proposed off-path leakage aware routing algorithm can reduce active leakage power in routing resources by 16.79%, and the increment of critical-path delay is only 1.06%.

关键词： Multiplexing Routing Wires Transistors Field programmable gate arrays Timing Vectors

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VMGuard: An Integrity Monitoring system for Management Virtual Machines

VMGuard: An Integrity Monitoring System for Management Virtu...

引用

International Conference on Parallel and Distributed systems (ICPADS)

作者： Haifeng Fang Yiqiang Zhao Hongyong Zang H. Howie Huang Ying Song Yuzhong Sun Zhiyong Liu University of the Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences China George Washington University Washington D.C. DC USA

A cloud computing provider can dynamically allocate virtual machines (VM) based on the needs of the customers, while maintaining the privileged access to the Management Virtual Machine that directly manages the hardware and supports the guest VMs. The customers must trust the cloud providers to protect the confidentiality and integrity of their applications and data. However, as the VMs from different customers are running on the same host, an attack to the management virtual machine will easily lead to the compromise of the guest VMs. Therefore, it is critical for a cloud computing system to ensure the trustworthiness of management VMs. To this end, we propose VMGuard, an integrity monitoring and detecting system for management virtual machines in a distributed environment. VMGuard utilizes a special VM, Guard Domain, which runs on each physical node to monitor the co-resident management VMs. The integrity measurements collected by the Guard Domains are sent to the VMGuard server for safe store and independent analysis. The experimental evaluation of a Xen-based prototype shows that VMGuard can quickly detect the root kit attacks while the performance overhead is low.

关键词： Driver circuits Kernel Servers Monitoring Engines Virtual machining Area measurement

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Multi-Cell Coordinated Scheduling and Power Allocation in Downlink LTE-A systems

Multi-Cell Coordinated Scheduling and Power Allocation in Do...

引用

IEEE Vehicular technology Conference

作者： Jian Yu Geoffrey Ye Li Changchuan Yin Suwen Tang Xiaolong Zhu Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Electrical and Computer Engineering Georgia Institute of Technology Huawei Shanghai Research Institute

ISBN: (纸本)9781479944484

In this paper, we investigate multi-cell coordinated scheduling and power allocation in downlink long term evolution advanced (LTE-A) systems, where orthogonal frequency division multiple-access (OFDMA) is used. The proposed scheme performs joint scheduling, power allocation, and modulation and coding scheme (MCS) selection to maximize the overall weighted throughput with proportional fairness. Our scheme considers the practical constraints in LTE-A systems. Since the optimization problem is a combinatorial and non-convex one and is with high complexity, low-complexity and suboptimal algorithms are proposed, which separate the scheduling and power allocation into two subproblems. Simulation results show that the proposed scheme can improve the average system throughput by 10% and the 10th percentile throughput by 15% compared with the existing scheme.

关键词： Resource management Throughput Interference Signal to noise ratio Indexes Optimization Scheduling

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