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Design and Implementation of Variable Precision Algorithm for Transcendental Functions

Journal of Physics: Conference Series 2019年第1期1325卷

作者： Hao Jiangwei Xu Jinchen Guo Shaozhong Xia Yuanyuan Liu Dan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

mathematical function is an essential part of numerical program, and it is also the key factor that affects the precision and performance of a program. In the design of mathematical functions, in order to satisfy most application scenarios, it is necessary to correctly round and cover the calculated interval as much as possible. But in certain applications, correct rounding and full coverage of definition domain may not be required. Therefore, mathematical functions can be customized according to application requirements to avoid precision waste as well as improve performance. However, manually implementing mathematical functions is a time-consuming and error-prone task. Tools like Metalibm is designed to automatically generate mathematical function code, which is difficult to take advantage of all the mathematical properties of a function and results in the generated code performing slowly than the corresponding mathematical library function. A method of generating variable precision code based on mathematical properties is proposed for the transcendental function in this work. Experiments showed the performance of the proposed method is comparable to Glibc mathematical function.

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A practical and aggressive loop fission technique 18th

A practical and aggressive loop fission technique

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18th International Conference on Algorithms and Architectures for Parallel Processing - ICA3PP 2018 Collocated Workshops: ICA3PP 2018 Workshop on Intelligent Algorithms for Large-scale Complex Optimization Problems, IALCOP 2018, ICA3PP 2018 Workshop on Security and Privacy in Data Processing, SPDP 2018

作者： Zhao, Bo Li, Yingying Han, Lin Zhao, Jie Gao, Wei Zhao, Rongcai Yahyapour, Ramin Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen Göttingen37077 Germany State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China French Institute for Research in Computer Science and Automation Rocquencourt France

ISBN: (纸本)9783030052331

Loop fission is an effective loop optimization for exploiting fine-grained parallelism. Currently, loop fission is widely used in existing parallelizing compilers. To fully exploit the optimization, we proposed and implemented a practical and aggressive loop fission technique. First, we present an aggressive dependence graph pruning method to eliminate pseudo dependences caused by the conservativeness of compilers. Second, we introduce a topological sort based loop fission algorithm to distribute loops correctly. Finally, to enhance the performance of the generated programs which have potential of loop fission, we propose an advanced loop fission strategy. We evaluate these techniques and algorithms in the experimental section. © Springer Nature Switzerland AG 2018.

关键词： Program compilers

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SARF: A Spark Based Recommendation Framework in the Cloud

SARF: A Spark Based Recommendation Framework in the Cloud

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International Conference on advanced Cloud and Big Data (CBD)

作者： Jieyao Zhou Yifei Lu Ka Bian Lei Qian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China

With the development of the IT industry, the information resources and services that people come into contact with have been growing. The sharing and interaction of these two types of information has led to further growth in data resulting in the advent of the era of big data. However, due to the complexity of information, such as low utilization, the confusion, and disorder, it will be a great challenge for service providers to offer customers services with high quality and accurate quality. The recommendation system is a solution adopted by e-marketers for such problems. This paper proposes a SPARK-based recommendation algorithm called SARF. The SARF first reads the original data from the HDFS and uses Spark to yield the datasets. Then, by using these datasets, the optimal recommended model is generated through iterative process. Finally, we gain the results which are recommended to the user by the optimal model. The final experimental results show that our SARF framework can effectively reduce the recommendation error and solve the problem of cold start.

关键词： Collaboration Filtering Sparks Big Data Matrix decomposition Training Filtering algorithms

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2020 roadmap on two-dimensional materials for energy storage and conversion

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Chinese Chemical Letters 2019年第12期30卷 2053-2064页

作者： Baolin Xu Shihan Qi Mengmeng Jin Xiaoyi Cai Linfei Lai Zhouting Sun Xiaogang Han Zifeng Lin Hui Shao Peng Peng Zhonghua Xiang Johan E.ten Elshof Rou Tan Chen Liu Zhaoxi Zhang Xiaochuan Duan Jianmin Ma School of Physics and Electronics Hunan UniversityChangsha 410082China Key Laboratory of Flexible Electronics&Institute of Advanced Materials(IAM) Jiangsu National Synergistic Innovation Center for Advanced Materials(SICAM)Nanjing Tech UniversityNanjing 210009China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore State Key Lab of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'an 710049China College of Materials Science and Engineering Sichuan UniversityChengdu 610065China UniversitéPaul Sabatier Laboratoire CIRIMAT UMR CNRS 5085Toulouse 31062France Réseau sur le Stockage Electrochimique de l’Energie(RS2E) CNRS 3459AmiensFrance State Key Laboratory of Organic–Inorganic Composites Beijing University of Chemical TechnologyBeijing 100029China University of Twente AE Enschede 7500the Netherlands Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361005China Key Laboratory of Materials Processing and Mold(Zhengzhou University) Ministry of EducationZhengzhou UniversityZhengzhou 450002China

Energy storage and conversion have attained significant intere st owing to its important applications that reduce CO2 emission through employing green *** promising technologies are included metalair batteries,metal-sulfur batteries,metal-ion batteries,electrochemical capacitors,***,metal elements are involved with lithium,sodium,and *** these devices,electrode materials are of importance to obtain high ***-dimensional(2 D) materials are a large kind of layered structured materials with promising future as energy storage materials,which include graphene,black phosporu s,MXenes,covalent organic frameworks(COFs),2 D oxides,2 D chalcogenides,and *** progress has been achieved to go ahead for 2 D materials in energy storage and *** researchers will join in this research *** the background,it has motivated us to contribute with a roadmap on ’two-dimensional materials for energy storage and conversion.

关键词： Graphene Black phosphorus MXenes Covalent organic frameworks Oxides Chalcogenides Metal-air batteries Metal-sulfur batteries Metal-ion batteries Electrochemical capacitors

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A Gene-Inspired Malware Detection Approach

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Journal of Physics: Conference Series 2019年第6期1168卷

作者： Yihang Chen Zheng Shan Fudong Liu Guanghui Liang Binglin Zhao Xingwei Li Meng Qiao State Key of Laboratory of Mathematical Engineering and Advanced Computing 450001 Zhengzhou Henan China

Malware detection is an important topic in cyber security. The research presented in this paper mainly studies on disassembly codes of Windows executable files, learns from the research route of bioinformatics and proposes the concept of software gene. A distance-based method is also proposed to measure the difference of genes and the dimensionality reduction based on a modified clustering algorithm of biological phylogenetic model. Finally a gene-inspired malware detector is constructed using Random Forest model. The software gene extraction proposed in this paper is more flexible and generates less data than the widely-used n-gram method. The detector based on genes also performs better. The clustering-based dimensionality reduction retains more comprehensive features and maintains the interpretability in software analysis area. The detector based on gene-inspired malware detection approach can reach the precision 96.14%, which is better than traditional methods.

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A Parallel Probabilistic Approach to Factorize a Semiprime

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American Journal of Computational Mathematics 2018年第2期8卷 175-183页

作者： Jianhui Li Department of Computer Science Guangdong Neusoft Institute Foshan City Foshan China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

In accordance with the distributive traits of semiprimes’ divisors, the article proposes an approach that can find out the small divisor of a semiprime by parallel computing. The approach incorporates a deterministic search with a probabilistic search, requires less memory and can be implemented on ordinary multicore computers. Experiments show that certain semiprimes of 27 to 46 decimal-bits can be validly factorized with the approach on personal computer in expected time.

关键词： Parallel Probabilistic Integer Factorization Semiprime

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ChanDet: Detection Model for Potential Channel of iOS Applications

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Journal of Physics: Conference Series 2019年第4期1187卷

作者： GuoMiao Zhou Ming Duan Qi Xi Hao Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450000 China

Despite providing iOS the security, comfortable, powerful mobile operating system, Apple has too many restrictions. Many users prefer to jailbreaking the iOS by using jailbreaking tool, which allows them to do more unavailable things on their devices. This behaviour may cause risks toward applications and many researches have focused on application security in various aspects. We find that a legal application (we call it potential channel hereafter) can be hijacked and it acts as a channel between the malware in device and the remote control terminal. In this paper, we introduce a channel model based on five conditions after analysing the entire operation procedure and comparing the similarities and differences of various applications. To approve our argument, we show iOS Messages which meets the five conditions, and demonstrate how to intercept messages, that means, a legal application can be hijacked and become a channel. To eliminate the risks, we propose a solution ChanDet and describe how to test whether an application is a potential channel or not. Finally, we give some protection strategies for applications, and we expect that ChanDet will play a significant role in application security of iOS.

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Path Sensitive Static Analysis of Taint-Style Vulnerabilities in PHP Code 17

Path Sensitive Static Analysis of Taint-Style Vulnerabilitie...

引用

2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Xue-Xiong Yan Qing-Xian Wang Heng-Tai Ma Institute of Software Chinese Academy of Sciences State Key Laboratory of Mathematical Engineering and Advanced Computing

ISBN: (纸本)9781509039456;9781509039449

this paper presents a novel path-sensitive static analysis method for detecting taint-style vulnerabilities in PHP code. The new method includes three key, the first of which is path searching during a basic block, the second is path searching between blocks, and the third is path searching crossing function call. A tool name POSE implements the new method and the testing results show the method is valid for taint-style vulnerabilities in PHP code.

关键词： path-sensitive PHP taint-style vulnerability POSE

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swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

arXiv

引用

arXiv 2019年

作者： Li, Mingzhen Liu, Changxi Liao, Jianjin Zheng, Xuegui Yang, Hailong Sun, Rujun Xu, Jun Gan, Lin Yang, Guangwen Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China National University of Singapore Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79× on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. Copyright © 2019, The Authors. All rights reserved.

关键词： Program compilers

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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