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arXiv 2020年

作者： Shen, Tong Zhang, Xiao-Wei Shang, Honghui Zhang, Min-Ye Wang, Xinqiang Wang, En-Ge Jiang, Hong Li, Xin-Zheng State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontier Science Center for Nano-optoelectronics School of Physics Peking University Beijing China International Center for Quantum Materials and School of Physics Peking University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing China Collaborative Innovation Center of Quantum Matter Peking University Beijing100871 China Ceramic Division Songshan Lake Lab Institute of Physics Chinese Academy of Sciences Guangdong China School of Physics Liaoning University Shenyang China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing China Collaborative Innovation Center of Quantum Matter Peking University Beijing China

We report ab initio band diagram and optical absorption spectra of hexagonal boron nitride (h-BN), focusing on unravelling how the completeness of basis set for GW calculations and how electron-phonon interactions (EPIs) impact on them. The completeness of basis set, an issue which was seldom discussed in previous optical spectra calculations of h-BN, is found crucial in providing converged quasiparticle band gaps. In the comparison among three different codes, we demonstrate that by including high-energy local orbitals in the all-electron linearized augmented plane waves based GW calculations, the quasiparticle direct and fundamental indirect band gaps are widened by ∼0.2 eV, giving values of 6.81 eV and 6.25 eV respectively at the GW0 level. EPIs, on the other hand, reduce them to 6.62 eV and 6.03 eV respectively at 0 K, and 6.60 eV and 5.98 eV respectively at 300 K. With clamped crystal structure, the first peak of the absorption spectrum is at 6.07 eV, originating from the direct exciton contributed by electron transitions around K in the Brillouin zone. After including the EPIs-renormalized quasiparticles in the Bethe-Salpeter equation, the exciton-phonon coupling shifts the first peak to 5.83 eV at 300 K, lower than the experimental value of ∼6.00 eV. This accuracy is acceptable to an ab initio description of excited states with no fitting parameter. Copyright © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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arXiv

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arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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Polymorphic PUF: Exploiting reconfigurability of CPU+FPGA SoC to resist modeling attack

Polymorphic PUF: Exploiting reconfigurability of CPU+FPGA So...

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2017 Asian Hardware Oriented Security and Trust Symposium, AsianHOST 2017

作者： Ye, Jing Gong, Yue Hu, Yu Li, Xiaowei State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781538614211

Physical Unclonable Function (PUF) is a promising hardware security primitive. However, it is severely threatened by modeling attacks. This paper proposes a novel Polymorphic PUF for CPU+FPGA SoC. We fully exploit the dynamic reconfigurability of the SoC and use the partial reconfiguration bitstream as the challenge to minimize the Challenge Response Pair (CRP) correlation so as to resist modeling attacks. An asymmetric RO pair is proposed to produce the response. Experiments on real CPU+FPGA SoCs show the high resistance of Polymorphic PUF against modeling attacks, with good uniformity and uniqueness. © 2017 IEEE.

关键词： system-on-chip

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STEED: An analytical database system for TrEE-structured data 43

STEED: An analytical database system for TrEE-structured dat...

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43rd International Conference on Very Large Data Bases, VLDB 2017

作者： Wang, Zhiyi Zhou, Dongyan Chen, Shimin State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

Tree-structured data formats, such as JSON and Protocol Buffers, are capable of expressing sophisticated data types, including nested, repeated, and missing values. While such expressing power contributes to their popularity in real-world applications, it presents a significant challenge for systems supporting tree-structured data. Existing systems have focused on general-purpose solutions either extending RDBMSs or designing native systems. However, the general-purpose approach often results in sophisticated data structures and algorithms, which may not reflect and optimize for the actual structure patterns in the real world. In this demonstration, we showcase Steed, an analytical database system for tree-structured data. We use the insights gained by analyzing representative real-world tree structured data as guidelines in the design of Steed. Steed learns and extracts a schema tree for a data set and uses the schema tree to reduce the storage space and improve the efficiency of data field accesses. We observe that sub-structures in real world data are often simple, while the treestructured data types can support very sophisticated structures. We optimize the storage structure, the column assembling algorithm, and the in-memory layout for the simple sub-structures (a.k.a. simple paths). Compared to representative state-of-the-art systems (i.e. PostgreSQL/JSON, MongoDB, and Hive+Parquet), Steed achieves orders of magnitude better performance for data analysis queries. © 2017 VLDB Endowment.

关键词： Database systems

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Data Motif-based Proxy Benchmarks for Big Data and AI Workloads

Data Motif-based Proxy Benchmarks for Big Data and AI Worklo...

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IEEE International Workshop/Symposium on Workload Characterization

作者： Wanling Gao Jianfeng Zhan Lei Wang Chunjie Luo Zhen Jia Daoyi Zheng Chen Zheng Xiwen He Hainan Ye Haibin Wang Rui Ren State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Princeton University Beijing Academy of Frontier Sciences and Technology Huawei

For the architecture community, reasonable simulation time is a strong requirement in addition to performance data accuracy. However, emerging big data and AI workloads are too huge at binary size level and prohibitively expensive to run on cycle-accurate simulators. The concept of data motif, which is identified as a class of units of computation performed on initial or intermediate data, is the first step towards building proxy benchmark to mimic the real-world big data and AI workloads. However, there is no practical way to construct a proxy benchmark based on the data motifs to help simulation based research. In this paper, we embark on a study to bridge the gap between data motif and a practical proxy benchmark. We propose a data motif-based proxy benchmark generating methodology by means of machine learning method, which combine data motifs with different weights to mimic the big data and AI workloads. Furthermore, we implement various data motifs using light-weight stacks and apply the methodology to five real-world workloads to construct a suite of proxy benchmarks, considering the data types, patterns, and distributions. The evaluation results show that our proxy benchmarks shorten the execution time by 100s times on real systems while maintaining the average system and micro-architecture performance data accuracy above 90%, even changing the input data sets or cluster configurations. Moreover, the generated proxy benchmarks reflect consistent performance trends across different architectures. To facilitate the community, we will release the proxy benchmarks on the project homepage http://***/BigDataBench.

关键词： Benchmark testing Big Data Artificial intelligence Measurement computer architecture Data models Computational modeling

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dCompaction： Speeding up Compaction of the LSM-Tree via Delayed Compaction

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Journal of computer Science & technology 2017年第1期32卷 41-54页

作者： Feng-Feng Pan Yin-Liang Yue Jin Xiong 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 Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China

key-value （KV） stores have become a backbone of large-scale applications in today＇s data centers. Write- optimized data structures like the Log-Structured Merge-tree （LSM-tree） and their variants are widely used in KV storage systems like BigTable and RocksDB. Conventional LSM-tree organizes KV items into multiple, successively larger components, and uses compaction to push KV items from one smaller component to another adjacent larger component until the KV items reach the largest component. Unfortunately, current compaction scheme incurs significant write amplification due to repeated KV item reads and writes, and then results in poor throughput. We propose a new compaction scheme, delayed compaction （dCompaction） that decreases write amplification, dCompaction postpones some compactions and gathers them into the following compaction. In this way, it avoids KV item reads and writes during compaction, and consequently improves the throughput of LSM-tree based KV stores. We implement dCompaction on RocksDB, and conduct extensive experiments. Validation using YCSB framework shows that compared with RocksDB, dCompaction has about 40% write performance improvements and also comparable read performance.

关键词： key-value store Log-Structured Merge-tree （LSM-tree） write amplification delayed compaction

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Landscape of big medical data: A pragmatic survey on prioritized tasks

arXiv

引用

arXiv 2019年

作者： Zhang, Zhifei Gao, Wanling Zhang, Fan Huang, Yunyou Dai, Shaopeng Fan, Fanda Zhan, Jianfeng Du, Mengjia Yin, Silin Xiong, Longxin Du, Juan Cheng, Yumei Zhou, Xiexuan Ren, Rui Wang, Lei Ye, Hainan Department of Physiology and Pathophysiology Capital Medical University State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Nanchang First Hospital Neonatal Center Beijing Children's Hospital Affiliated to Capital Medical University Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University Beijing Academy of Frontier Sciences and Technology

Big medical data poses great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of state-of-the-practice and state-of-the-art systems handling big medical data currently or in future? Finally but not least, are we, life scientists, clinicians, computer scientists and engineers, ready for working together? We believe answering the above issues will help define and shape the landscape of big medical data. Copyright © 2019, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Efficient dynamic evolution of service composition

Efficient dynamic evolution of service composition

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作者： Lv, Chen Jiang, Wei Hu, Songlin Wang, Jiye Lu, Guoliang Liu, Zhiyong School of Information Science and Engineering Shandong Normal University China Greatwall Drilling Company R&D Academy of Well Logging CNPC Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Information Technology State Grid Corporation of China Beijing China School of Mechanical Engineering Shandong University China Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

Current QoS-aware automatic service composition queries over a network of web services are often one-time in nature. After a network of web services is built, such queries are issued once, and answers are found from the scratch. The underlying assumption is that the participating web services are rather static, which means that their functional and non-functional parameters seldom change. However, such an assumption is often baseless. New services come and go, service APIs change gradually, and QoS values fluctuate in practice. Therefore, a support for efficiently adapting service composition is desired. In this paper, we propose an event driven continuous query algorithm to intelligently cope with different types of dynamic services, and thus enable evolution of service composition. Evaluation using both real QoS data and synthetic web service data shows its superior performance, compared with the state-of-the-art solution which won the performance championship of Web Service Challenge in 2009 and 2010. Moreover, we generalize a new graph problem: dynamic single-source optimal-Directed Acyclic Graphs (DAG) problem based on the above work. It is argued that API recommendation for framework evolution, as another typical application, could also be modeled and addressed efficiently using the proposed new graph approach. © 2017 IEEE.

关键词： Web services

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Auto-tuning neural network quantization framework for collaborative inference between the cloud and edge

arXiv

引用

arXiv 2018年

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

Recently, deep neural networks (DNNs) have been widely applied in mobile intelligent applications. The inference for the DNNs is usually performed in the cloud. However, it leads to a large overhead of transmitting data via wireless network. In this paper, we demonstrate the advantages of the cloud-edge collaborative inference with quantization. By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed. We study the effectiveness of mixed-precision collaborative inference of state-of-the-art DNNs by using ImageNet dataset. The experimental results show that our framework can generate reasonable network partitions and reduce the storage on mobile devices with trivial loss of accuracy. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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An Efficient Network-on-Chip Router for Dataflow architecture

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Journal of computer Science & technology 2017年第1期32卷 11-25页

作者： Xiao-Wei Shen Xiao-Chun Ye Xu Tan Da Wang Lunkai Zhang Wen-Ming Li Zhi-Min Zhang Dong-Rui Fan Ning-Hui Sun State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Department of Computer Science The University of Chicago IL 60637 U.S.A.

Dataflow architecture has shown its advantages in many high-performance computing cases. In dataflow computing, a large amount of data are frequently transferred among processing elements through the network-on-chip （NoC）. Thus the router design has a significant impact on the performance of dataflow architecture. Common routers are designed for control-flow multi-core architecture and we find they are not suitable for dataflow architecture. In this work, we analyze and extract the features of data transfers in NoCs of dataflow architecture： multiple destinations, high injection rate, and performance sensitive to delay. Based on the three features, we propose a novel and efficient NoC router for dataflow architecture. The proposed router supports multi-destination; thus it can transfer data with multiple destinations in a single transfer. Moreover, the router adopts output buffer to maximize throughput and adopts non-flit packets to minimize transfer delay. Experimental results show that the proposed router can improve the performance of dataflow architecture by 3.6x over a state-of-the-art router.

关键词： multi-destination router network-on-chip dataflow architecture high-performance computing

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