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12th International Symposium on Computational Intelligence and Design, ISCID 2019

作者： Xie, Mingye Tan, Long Heilongjiang University Colleage of Computer Science and Technology China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781728146522

In recent years, due to the increasing demand for real-time data processing, data mining on stream gradually becomes a research hotspots, mining over uncertain data stream is a more practical compare to over precise data stream, which doesn't take uncertainty of data in real world into consideration. Most of the popular algorithms of uncertain data stream mining are based on tree structure, all branches in the tree need be retrieved to find all frequent patterns while is time-consuming. Considering this defect of tree structure, a new structure Uncertain Item-lists (UIT-lists) and a efficient Uncertain Frequent Stream mining algorithm(UFS-mine) is presented. Experiments show our approach has great performence on runtime and with same memory useage compare to an up-to-state uncertain stream data mining algorithm. © 2019 IEEE.

关键词： Data mining

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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Ensemble Strategy Based Hyper-heuristic Evolutionary Algorithm for Many-Objective Optimization 13th

Ensemble Strategy Based Hyper-heuristic Evolutionary Algorit...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Qian, Wang Jingbo, Zhang Zhihua, Cui Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan030024 China

ISBN: (纸本)9783031578076

Many-objective Optimization problems (MaOPs), with four or more objectives are difficult to solve, is a kind of common optimization problems in actual industrial production. In recent years, a large number of many-objective optimization algorithms (MaOEAs) have been proposed to solve various types of MaOPs. However, in practical problems, it is usually hard to improve the existing optimization algorithms or make a lot of attempts for MaOEAs because the true Pareto surface is usually unknown in a new MaOPs, which is a time-consuming and uncertain task. In this paper, inspired by the selective hyper heuristic optimization algorithm, we propose an integrated hyper-heuristic many-objective optimization algorithm (MaOEA-EH), which can integrate the existing advanced MaOEAs by simulating the PBFT consensus mechanism in the blockchain, and select the best algorithm for the current problem through the voting-election method in the iterative process. Numerical results show that our algorithm performs well on various many-objective problems. © IFIP International Federation for Information Processing 2024.

关键词： Evolutionary algorithms

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Perspectives of China's HPC system development:a view from the 2009 China HPC TOP100 list

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中国计算机科学前沿 2010年第4期4卷 437-444页

作者： Yunquan ZHANG Jiachang SUN Guoxing YUAN Linbo ZHANG Laboratory of Parallel Computing Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Chinese Academy of SciencesBeijing 100190China Institute of Applied Physics and Computational Mathematics Beijing 100088China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

The China HPC TOP100 list, an annual report of the 100 most powerful high performance computing (HPC) systems installed in mainland China, has traced the rapid growth of HPC technology in China since its first publication in 2002. This paper introduces the China HPC TOP100 list and reviews the current status of HPC systems in China in terms of system features, manufactures, and areas of application using the data reported in the most recent list, published on November 1st, 2009. We provide further analysis, prediction of future trends, and directions of the development of HPC systems in China referencing historical data accumulated through archived TOP100 lists and other publically available *** predict that the aggregated Linpack performance of the top 100 HPC systems will reach 10 PFlops in 2011, a single system with 10 PFlops peak performance will appear between 2012 and 2013, the aggregated performance of the top 100 systems will reach 100 PFlops in 2014, and a single system with 100 PFlops peak performance will appear around 2015.

关键词： high performance computer (HPC) China HPC TOP 100 list Linpack performance trend analysis

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Regulatory Element parallel Mining Algorithm Based on Bit Combination 12

Regulatory Element Parallel Mining Algorithm Based on Bit Co...

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12th International Symposium on Computational Intelligence and Design, ISCID 2019

作者： Zhou, Kailong Lu, Jun Zhao, Renpeng Lv, Xingfeng Heilongjiang University College of Computer Science and Technology Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781728146522

Regulatory element parallel mining algorithm of frequent itemset based on bit combination is proposed in this paper. The algorithm is represented by converting promoter data filtered by frequent itemset into binary machine bits, and then mining the frequent itemset of the data set in parallel by means of regulatory element. In the whole mining process, if a combination of regulatory elements is frequent, data should be added 1, and if it is not frequent, the data should be pruned. Preprocessing, eliminating and other operations are used to optimize the whole algorithm. Converting the processed raw data and regulatory element into binary bits facilitates the parallel operation of data and saves memory space, breaking through the limitations of traditional methods that cannot be effectively parallelized due to recursive operations in the mining process. In this thesis, OpenMP technology is used to parallelize the algorithm based on regulatory element, which improves the efficiency of the algorithm. © 2019 IEEE.

关键词： Data mining

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GraphPad: Optimized Graph Primitives for parallel and Distributed Platforms

GraphPad: Optimized Graph Primitives for Parallel and Distri...

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International Symposium on parallel and Distributed Processing (IPDPS)

作者： Michael J. Anderson Narayanan Sundaram Nadathur Satish Md. Mostofa Ali Patwary Theodore L. Willke Pradeep Dubey Intel Corporation Parallel Computing Laboratory

ISBN: (纸本)9781509021413

The duality between graphs and matrices means that many common graph analyses can be expressed with primitives such as generalized sparse matrix-vector multiplication (SpMSpV) and sparse matrix-matrix multiplication (SpGEMM). Achieving high performance on these primitives is challenging due to limited arithmetic intensity, irregular memory accesses, and significant network communication requirements in the distributed setting. In this paper we implement four graph applications using GraphPad, our optimized multinode implementations of generalized linear algebra primitives such as SpMSpV and SpGEMM. GraphPad is highly flexible to accommodate multiple data layouts, partitioning strategies, and incorporates communication optimizations. Our performance at scale can exceed that of CombBLAS by up to 40×. In addition to GraphPad's performance in a distributed setting, it is also within 2× the performance of GraphMat, a high performance graph framework on a single node for four out of five benchmarks. We also show our communication optimizations and flexibility are critical for good performance on both HPC clusters and commodity cloud platforms.

关键词： Sparse matrices Matrices Kernel Optimization Algorithm design and analysis Programming

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PerfMiner: Cluster-wide collection, storage and presentation of application level hardware performance data

PerfMiner: Cluster-wide collection, storage and presentation...

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11th International Euro-Par Conference, Euro-Par 2005

作者： Mucci, Philip J. Ahlin, Daniel Danielsson, Johan Ekman, Per Malinowski, Lars Innovative Computing Laboratory University of Tennessee Knoxville TN United States Center for Parallel Computers Royal Institute of Technology Stockholm Sweden

We present PerfMiner, a system for the transparent collection, storage and presentation of thread-level hardware performance data across an entire cluster. Every sub-process/thread spawned by the user through the batch system is measured with near zero overhead and no dilation of run-time. Performance metrics are collected at the thread level using tool built on top of the Performance Application Programming Interface (PAPI). As the hardware counters are virtualized by the OS, the resulting counts are largely unaffected by other kernel or user processes. PerfMiner correlates this performance data with metadata from the batch system and places it in a database. Through a command line and web interface, the user can make queries to the database to report information on everything from overall workload characterization and system utilization to the performance of a single thread in a specific application. This is in contrast to other monitoring systems that report aggregate system-wide metrics sampled over a period of time. In this paper, we describe our implementation of PerfMiner as well as present some results from the test deployment of PerfMiner across three different clusters at the Center for parallel Computers at The Royal Institute of Technology in Stockholm, Sweden. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Computer hardware

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11th International Conference on Information Science and Technology, ICIST 2021

作者： Wang, Zhongqing Yang, Yan Zhong, Yingli Heilongjiang University School of Computer Science Technology Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781665412667

Graph similarity search is an important research problem in many applications, such as finding result graphs that have a similar structure to a given entity in biochemistry, data mining, and pattern recognition. Top-k graph similarity search is one of graph similarity search tasks, which aims to find the top-k graphs that are most similar to the query graph in a given graph database. In this paper, the top-k similarity search problem based on the graph edit distance is studied according to the corollary of the partitioned similarity theorem. Firstly, in order to speed up the online search process and avoid scanning each graph in the database one by one, an offline hierarchical inverted index is constructed to satisfy top-k search. Secondly, the offline hierarchical inverted index is used to filter the candidate graphs online and verify them, which reduces the time of searching the graphs. Finally, the good performance of the algorithm in running time and scalability is verified by running the similarity algorithm on real dataset and synthetic dataset. © 2021 IEEE.

关键词： Graph Databases

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An Incremental Partitioning Graph Similarity Search Based on Tree Structure Index 6th

An Incremental Partitioning Graph Similarity Search Based on...

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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Li, Yuhang Yang, Yan Zhong, Yingli School of Computer Science Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9789811579806

Graph similarity search is a common operation of graph database, and graph editing distance constraint is the most common similarity measure to solve graph similarity search problem. However, accurate calculation of graph editing distance is proved to be NP hard, and the filter and verification framework are adopted in current method. In this paper, a dictionary tree based clustering index structure is proposed to reduce the cost of candidate graph, and is verified in the filtering stage. An efficient incremental partition algorithm was designed. By calculating the distance between query graph and candidate graph partition, the filtering effect was further enhanced. Experiments on real large graph datasets show that the performance of this algorithm is significantly better than that of the existing algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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