Fujiang Ao

Publications (9)0 Total impact

• Conference Proceeding: An Efficient Algorithm for Mining Closed Frequent Itemsets in Data Streams

  Fujiang Ao, Jing Du, Yuejin Yan, Baohong Liu, Kedi Huang
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  ABSTRACT: Mining closed frequent itemsets in the sliding window is one of important topics of data streams mining. In this paper, we propose a novel algorithm, FPCFI-DS, which mines closed frequent itemsets in the sliding window of data streams efficiently, and maintains the precise closed frequent itemsets in the current window at any time. The algorithm uses a single-pass lexicographical-order FP-Tree-based algorithm with mixed item ordering policy to mine the closed frequent itemsets in the first window, and introduces a novel updating approach to process the sliding of window. The experimental results show that FPCFI-DS performs better than the state-of-the-art algorithm Moment in terms of both the time and space efficiencies, especially for dense dataset or low minimum support.
  Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on; 08/2008
• Conference Proceeding: Model-guided strip size selection for minimal execution time on imagine stream processor

  Jing Du, Yuhua Tang, Fujiang Ao, Tao Tang, Xuejun Yang
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  ABSTRACT: Strip-mining is a critical optimization for improving the effectiveness of memory hierarchy of Imagine. In this paper, we present an efficient compiler algorithm for selecting the optimal strip size to minimize the execution time of stream programs. First, we build a graceful analytical model that characterizes the effect of strip size on key performance factors. Then, we design a novel algorithm for selecting optimal strip size according to the model analysis and apply it to some stream programs. Furthermore, we implement the algorithm in the stream compiler. The experimental results show that when the algorithm is used, the execution time is close to the experimentally best. It is certain that our algorithm can efficiently exploit the tremendous potential of Imagine.
  Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on; 08/2008
• Conference Proceeding: OSS: Efficient Compiler Approach for Selecting Optimal Strip Size on the Imagine Stream Processor

  Jing Du, Canqun Yang, Fujiang Ao, Xuejun Yang
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  ABSTRACT: Strip-mining technique is critical for improving performance of large-scale scientific applications on Imagine. In this paper, we present a model-guided strip size selection approach (OSS) for finding the optimal strip size to minimize the execution time. Our strategy consists of a detailed analytical model that characterizes the effect of strip size on program behavior. Then according to the model analysis, we design a simple strip size selection strategy, that is, the optimal strip size is 512 words. Our experimental results show that when the optimal strip size is used, the execution time is close to the experimentally best. It is certain that our strategy can efficiently exploit the tremendous potential of Imagine.
  Advanced Information Networking and Applications - Workshops, 2008. AINAW 2008. 22nd International Conference on; 04/2008
• Conference Proceeding: MV-FT: Efficient Implementation for Matrix-Vector Multiplication on FT64 Stream Processor

  Jing Du, Fujiang Ao, Xuejun Yang
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  ABSTRACT: In this paper, we present a detailed case study of the optimizing implementation of a fundamental scientific kernel, matrix-vector multiplication, on FT64, which is the first 64-bit stream processor designed for scientific computing. The major novelties of our study are as follows. First, we develop four stream programs according to different stream organizations, involving dot product, row product, multi-dot product and multi-row product approaches. Second the optimal strip size for partitioning the large matrix is put forward based on a practical parameter model. Finally loop unrolling and software pipelining are used to hide the communications with the computations. The experimental results show that the optimizing implementations on FT64 achieve high speedup over the corresponding Fortran programs running on Itanium 2. It is certain that matrix-vector multiplication can efficiently exploit the tremendous potential of FT64 stream processor through programming optimizations.
  Digital Society, 2008 Second International Conference on the; 03/2008
• Conference Proceeding: An Efficient Strip-Mining Algorithm for Improving SRF Bandwidth Utilization on Imagine

  Wenjing Yang, Jing Du, Fujiang Ao, Xuejun Yang
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  ABSTRACT: Strip-mining is a crucial technique for memory hierarchy optimization. In this paper, we propose an efficient strip-mining algorithm for improving SRF bandwidth utilization on Imagine. Firstly, we present how to determine the optimal kernel set for strip-mining. The process is based on a novel structure proposed by us, namely kernel reuse graph. Secondly, we select the optimal strip size, so as to achieve the tradeoff between stream reuse and stream prefetching. Finally, we propose the efficient strip-mining algorithm, which is implemented in SCompiler. The experiment results show that our strip-mining algorithm is a practical and promising solution to improve SRF locality and hide the memory access overhead effectively on Imagine.
  Signal-Image Technologies and Internet-Based System, 2007. SITIS '07. Third International IEEE Conference on; 01/2008
• Conference Proceeding: A Novel Pruning Technique for Mining Maximal Frequent Itemsets

  Fujiang Ao, Yuejin Yan, Jian Huang, Kedi Huang
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  ABSTRACT: Maximal frequent itemsets (MFIs) mining is important for many applications. To improve the performance of the MFI algorithms, the key is to use appropriate pruning techniques which can maximally reduce the searching space of the algorithm. In this paper, we present a novel pruning technique, subset equivalence pruning. To mining MFIs in data streams, we reconstruct the FPmax* algorithm to a single-pass algorithm, named FPmax*-DS. Subset equivalence pruning technique is added in FPmax*-DS. The experiments show that the pruning technique can efficiently reduce the searching space. Especially for some dense datasets, the size of searching space can be trimmed off by about 40%.
  Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on; 09/2007
• Conference Proceeding: Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree.

  Fujiang Ao, Yuejin Yan, Jian Huang, Kedi Huang
  Machine Learning and Data Mining in Pattern Recognition, 5th International Conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007, Proceedings; 01/2007
• Conference Proceeding: Scientific Computing Applications on the Imagine Stream Processor.

  Jing Du, Xuejun Yang, Guibin Wang, Fujiang Ao
  Advances in Computer Systems Architecture, 11th Asia-Pacific Conference, ACSAC 2006, Shanghai, China, September 6-8, 2006, Proceedings; 01/2006
• Article: Implementation and Evaluation of Matrix-matrix Multiplication on FT64 Stream Processor

  Wenjing Yang, Jing Du, Fujiang Ao, Xuejun Yang
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  ABSTRACT: The FT64 stream processor is a 64-bit stream processor for scientific computing. It is necessary to research efficient implementation of scientific applications on FT64 to exploit the powerful ability. Matrix-matrix multiplication is an important kernel used in many scientific applications. In this paper, we develop two stream implementations of matrix-matrix multiplication on FT64, and optimize these versions by using stripmining technique. Our efforts aim at reducing memory access overhead and improving computational intensiveness. The experimental results show that the optimizing implementations on FT64 achieve high speedup over the corresponding fortran programs running on Itanium 2. It is certain that matrix-matrix multiplication can efficiently exploit the tremendous potential of FT64 processor through programming optimizations.
  Convergence Information Technology, International Conference on.