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6th Workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 2015

作者： Zhao, Liutao Gao, Wanling Jin, Yi Key Laboratory of Cloud Computing Technology and Application Beijing Computing Center Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319290058

Benchmarking as yardsticks for system design and evaluation, has developed a long period and plays a pivotal role in many domains, such as database systems and high performance computing. Through prolonged and unremitting efforts, benchmarks on these domains have been reaching their maturity gradually. However, in terms of emerging scenarios of big data, its different properties in data volume, data types, data processing requirements and techniques, make that existing benchmarks are rarely appropriate for big data systems and further make us wonder how to define a good big data benchmark. In this paper, we revisit successful benchmarks in other domains from two perspectives: benchmarking principles which define fundamental rules, and methodologies which guide the benchmark constructions. Further, we conclude the benchmarking principle and methodology on big data benchmarking from a recent open-source effort – BigDataBench. © Springer International Publishing Switzerland 2016.

关键词： Benchmarking

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325 MHz and near transform-limited pulse output directly from an Er:fiber ring laser

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Chinese Optics Letters 2013年第6期11卷 43-45页

作者：戚红霞张健周耕稷王爱民张志刚 State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Engineering and Computer SciencePeking University

A compact Er：fiber ring laser operated at a fundamental repetition rate of 325 MHz is reported. Two gain fibers with opposite dispersion are employed to shorten the fiber laser cavity for high repetition rate and soliton-like pulse generation without losing gain and compactness. The spectral bandwidth of the output pulse is 24 nm and the direct pulse duration is 123 fs without extra-cavity compression, which are values near the transform-Hmited range.

关键词： Bandwidth compression Erbium Fiber lasers Pulse generators Solitons

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Localized space-tim e method of fundamental solutions for three-dimensional transient diffusion problem

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Acta Mechanica Sinica 2020年第5期36卷 1051-1057,I0002页

作者： L.Qiu J.Lin Q.-H.Qin W.Chen International Center for Simulation Software in Engineering and Sciences College of Mechanics and MaterialsHohai UniversityNanjing 210098China College of Engineering and Computer Science Australian National UniversityCanberraACT 2601Australia State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Acoustic Institute of AcousticsChinese Academy of SciencesBeijing 100190China

A localized space-time method of fundamental solutions(LSTMFS)is extended for solving three-dimensional transient diffusion problems in this *** interval segmentation in temporal direction is developed for the accurate simulation of long-time-dependent diffusion *** the LSTMFS,the whole space-time domain with nodes arranged i divided into a series of overlapping subdomains with a simple *** each subdomain,the conventional method of fundamental solutions is utilized and the coefficients associated with the considered domain can be explicitly *** calculating a combined sparse matrix system,the value at any node inside the space-time domain can be *** experi-ments demonstrate that high accuracy and efficiency can be simultaneously achieved via the LSTMFS,even for the problems defined on a long-time and quite complex computational domain.

关键词： Localized spacetime method of fundamental solutions Meshless method Long-time evolution Transient diffusion

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Cloud-based manufacturing execution system: Case study FMS

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International Journal of Industrial and systems Engineering 2018年第4期30卷 449-467页

作者： Barenji, Ali Vatankhah Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System School of Electromechanical Engineering Guangdong University of Technology Guangzhou510006 China Mechatronics Engineering Department Kennesaw State University MariettaGA United States

This paper proposed a cloud based manufacturing execution system (CbMES) for covering existing gaps between manufacturing execution system(MES) and enterprise resource planning(ERP) in the small and medium sized enterprises. The proposed system used a frame work based on the model driven architecture (MDA) for evaluation the proposed system, a flexible manufacturing system is selected as the case study. The proposed CbMES is developed for flexible manufacturing system and has been implemented within the oracle cloud platform, for the evaluation of this system, a simulation platform based on the colour Petri net is utilised. The result of simulation indicates that the proposed CbMAS is capable of closing the gaps between MES and ERP;therefore, the outcome of the results showed that the uptime productivity and the production rate of flexible manufacturing systems increased considerably meaning that the system is an effective and viable solution for the IT infrastructure. © 2018 Inderscience Enterprises Ltd.

关键词： Enterprise resource planning

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Persistent B+-Trees in non-volatile main memory 41st

Persistent B+-Trees in non-volatile main memory

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41st International Conference on Very Large Data Bases, VLDB 2015

作者： Chen, Shimin Jin, Qin State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Computer Science Department School of Information Renmin University of China China

computer systems in the near future are expected to have Non- Volatile Main Memory (NVMM), enabled by a new generation of Non-Volatile Memory (NVM) technologies, such as Phase Change Memory (PCM), STT-MRAM, and Memristor. The non-volatility property has the promise to persist in-memory data structures for instantaneous failure recovery. However, realizing such promise requires a careful design to ensure that in-memory data structures are in known consistent states after failures. This paper studies persistent in-memory B+-Trees as B+-Trees are widely used in database and data-intensive systems. While traditional techniques, such as undo-redo logging and shadowing, support persistent B+-Trees, we find that they incur drastic performance overhead because of extensive NVM writes and CPU cache flush operations. PCM-friendly B+-Trees with unsorted leaf nodes help mediate this issue, but the remaining overhead is still large. In this paper, we propose write atomic B+-Trees (wB+-Trees), a new type of main-memory B+-Trees, that aim to reduce such overhead as much as possible. wB+-Tree nodes employ a small indirect slot array and/or a bitmap so that most insertions and deletions do not require the movement of index entries. In this way, wB+- Trees can achieve node consistency either through atomic writes in the nodes or by redo-only logging. We model fast NVM using DRAM on a real machine and model PCM using a cycle-accurate simulator. Experimental results show that compared with previous persistent B+-Tree solutions, wB+-Trees achieve up to 8.8x speedups on DRAM-like fast NVM and up to 27.1x speedups on PCM for insertions and deletions while maintaining good search performance. Moreover, we replaced Memcached's internal hash index with tree indices. Our real machine Memcached experiments show that wB+-Trees achieve up to 3.8X improvements over previous persistent tree structures with undo-redo logging or shadowing. © 2015 VLDB Endowment 21508097/15/03.

关键词： Dynamic random access storage

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Improving DNN Accuracy on MLC PIM via Black-box Fine-tuning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2024年第6期44卷 2277-2286页

作者： Lv, Hao Zhang, Lei Wang, Ying Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100089 China The State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100089 China

Resistive random access memory (RRAM) emerges as a promising technology for developing energy-efficient deep neural network (DNN) accelerators, owing to its analog computing paradigm for matrix-vector multiplication. However, the inherent non-ideal device features of RRAM cells, such as device variation, read disturbances, and limited on/off ratio, present challenges for model deployment. Therefore, to ensure accurate storage and computing precision for RRAM-based accelerators, a widely used practice is encoding a DNN weight by multiple cells, resulting in significant memory overhead and underutilization. This challenge is further exacerbated by the rapid increases in model size witnessed in recent years. While the one-to-one weight-cell mapping strategy can improve memory utilization, it inevitably introduces deviations in the mapped DNN weight from the desired value due to RRAM variation issues, leading to model accuracy degradation. In response to this challenge, we abstract the model optimization on RRAM chips as a black-box optimization problem, aimed at optimizing model accuracy without the requirement of precise weight programming. We systematically analyze the model optimization behavior on multi-level RRAM devices by investigating the accuracy recovery process of various fine-tuning strategies in recovering model performance under the black-box setting. Based on the analysis, we propose a black-box finetune scheme to recover the model performance for multilevel RRAM under the black-box setting. Our proposed scheme leverages knowledge distillation and exploits input/output information of the model on RRAM to guide the fine-tuning process, finally restoring its accuracy. Experimental results demonstrate the efficacy of our black-box fine-tuning scheme, achieving nearly complete recovery of model performance. Our approach yields over a 3% improvement in model accuracy compared to variation-aware training approaches. © 1982-2012 IEEE.

关键词： Energy efficiency

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Cloud Computing Service: The Caseof Large Matrix Determinant Computation

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IEEE Transactions on Services Computing 2015年第5期8卷 688-700页

作者： Lei, Xinyu Liao, Xiaofeng Huang, Tingwen Li, Huaqing State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Computer Science Chongqing University Chongqing400044 China Texas A and M University at Qatar PO Box 23874 Doha Qatar

Cloud computing paradigm provides an alternative and economical service for resource-constrained clients to perform large-scale data computation. Since large matrix determinant computation (DC) is ubiquitous in the fields of science and engineering, a first step is taken in this paper to design a protocol that enables clients to securely, verifiably, and efficiently outsource DC to a malicious cloud. The main idea to protect the privacy is employing some transformations on the original matrix to get an encrypted matrix which is sent to the cloud;and then transforming the result returned from the cloud to get the correct determinant of the original matrix. Afterwards, a randomized Monte Carlo verification algorithm with one-sided error is introduced, whose superiority in designing inexpensive result verification algorithm for secure outsourcing is well demonstrated. In addition, it is analytically shown that the proposed protocol simultaneously fulfills the goals of correctness, security, robust cheating resistance, and high-efficiency. Extensive theoretical analysis and experimental evaluation also show its high-efficiency and immediate practicability. It is hoped that the proposed protocol can shed light in designing other novel secure outsourcing protocols, and inspire powerful companies and working groups to finish the programming of the demanded all-inclusive scientific computations outsourcing software system. It is believed that such software system can be profitable by means of providing large-scale scientific computation services for so many potential clients. © 2014 IEEE.

关键词： Outsourcing

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Fuzzy tracking control design for hypersonic vehicles via T-S model

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Science China(Information Sciences) 2011年第3期54卷 521-528页

作者： GAO DaoXiang 1 & SUN ZengQi 2 1 School of Technology, Beijing Forestry University, Beijing 100083, China 2 state key laboratory of Intelligent Technology and system, Department of computer Science and Technology, Tsinghua University, Beijing 100084, China 1. School of Technology Beijing Forestry University Beijing 100083 China2. State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua University Beijing 100084 China

The main focus of this paper is on designing a T-S fuzzy controller for the hypersonic vehicle. The longitudinal dynamics of the vehicle are studied using time-scale decomposition to reduce the complexity of T-S modeling. The dynamic inversion with PI control technique is applied for the slow dynamics to derive the flight path angle command and throttle setting by taking the pilot altitude and velocity command as its inputs. The T-S fuzzy controller is designed for the fast dynamics to derive the elevator deflection to track the flight path angle command. The discrepancy between the T-S model and real vehicle model is considered by using sliding mode control for the system stability. Simulation results are included to show the effectiveness of the controller.

关键词： fuzzy control T-S model sliding mode control hypersonic vehicle

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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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Optimizing Parallel Kinetic Monte Carlo Simulation by Communication Aggregation and Scheduling

Optimizing Parallel Kinetic Monte Carlo Simulation by Commun...

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2015大数据技术及应用论坛

作者： Baodong Wu Shigang Li Yunquan Zhang State Key Laboratory of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences

Kinetic Monte Carlo(KMC) algorithm has been widely applied for simulation of radiation damage, grain growth and chemical reactions. To simulate at a large temporal and spatial scale, domain decomposition is commonly used to parallelize the KMC algorithm. However, through experimental analysis, we find that the communication overhead is the main bottleneck which affects the overall performance and limits the scalability of parallel KMC algorithm on large-scale clusters. To alleviate the above problems, we present a communication aggrega‐tion approach to reduce the total number of messages and eliminate the commu‐nication redundancy, and further utilize neighborhood collective operations to optimize the communication scheduling. Experimental results show that the opti‐mized KMC algorithm exhibits better performance and scalability compared with the well-known open-source library—SPPARKS. On 32-node Xeon E5-2680 cluster(total 640 cores), the optimized algorithm reduces the total execution time by 16 %, reduces the communication time by 50 % on average, and achieves 24 times speedup over the single node(20 cores) execution.

关键词： Domain decomposition Communication aggregation Communication scheduling Neighborhood collectives

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