On sparse evaluation representations

被引:13
|
作者
Ramalingam, G [1 ]
机构
[1] IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA
关键词
sparse evaluation graphs; static single assignment forms; dataflow analysis; graph transformations; quick propagation graphs; equivalent flow graphs; partially equivalent flow graphs;
D O I
10.1016/S0304-3975(00)00315-7
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The sparse evaluation graph has emerged over the past several years as an intermediate representation that captures the dataflow information in a program compactly and helps perform dataflow analysis efficiently. The contributions of this paper are three-fold: We present a linear time algorithm for constructing a variant of the sparse evaluation graph for any dataflow analysis problem. Our algorithm has two advantages over previous algorithms for constructing sparse evaluation graphs, First. it is simpler to understand and implement, Second. our algorithm generates a more compact representation than the one generated by previous algorithms, (Our algorithm is also as efficient as the most efficient known algorithm for the problem.) We present a formal definition of an equivalent flow, graph, which attempts to capture the goals of sparse evaluation. We present a quadratic algorithm for constructing an equivalent flow graph consisting of the minimum number of vertices possible. We show that the problem of constructing an equivalent flow graph consisting of the minimum number of vertices and edges is NP-hard. We generalize the notion of an equivalent flow graph to that of a partially equivalent flow graph, an even more compact representation, utilizing the fact that the dataflow solution is not required at every node of the control-flow graph. We also present an efficient linear time algorithm for constructing a partially equivalent flow graph. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:119 / 147
页数:29
相关论文
共 50 条
  • [41] Efficient Algorithms for Convolutional Sparse Representations
    Wohlberg, Brendt
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2016, 25 (01) : 301 - 315
  • [42] Sparse representations for limited data tomography
    Liao, Hstau Y.
    Sapiro, Guillermo
    2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4, 2008, : 1375 - +
  • [43] Spatial Relationships over Sparse Representations
    Lomenie, Nicolas
    Racoceanu, Daniel
    2009 24TH INTERNATIONAL CONFERENCE IMAGE AND VISION COMPUTING NEW ZEALAND (IVCNZ 2009), 2009, : 226 - 230
  • [44] 3-D Sparse Representations
    Lanusse, Francois
    Starck, Jean-Luc
    Woiselle, Arnaud
    Fadili, M. Jalal
    ADVANCES IN IMAGING AND ELECTRON PHYSICS, VOL 183, 2014, 183 : 99 - 204
  • [45] BOUNDARY HANDLING FOR CONVOLUTIONAL SPARSE REPRESENTATIONS
    Wohlberg, Brendt
    2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2016, : 1833 - 1837
  • [46] Sparse geometric image representations with bandelets
    Le Pennec, E
    Mallat, S
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2005, 14 (04) : 423 - 438
  • [47] Novelty Detection in Images by Sparse Representations
    Boracchi, Giacomo
    Carrera, Diego
    Wohlberg, Brendt
    2014 IEEE SYMPOSIUM ON INTELLIGENT EMBEDDED SYSTEMS (IES), 2014, : 47 - 54
  • [48] Role of Homeostasis in Learning Sparse Representations
    Perrinet, Laurent U.
    NEURAL COMPUTATION, 2010, 22 (07) : 1812 - 1836
  • [49] Approximate Search with Quantized Sparse Representations
    Jain, Himalaya
    Perez, Patrick
    Gribonval, Remi
    Zepeda, Joaquin
    Jegou, Herve
    COMPUTER VISION - ECCV 2016, PT VII, 2016, 9911 : 681 - 696
  • [50] Decomposition of MEG signals with sparse representations
    Okurt, Tolga E.
    Sun, Mingui
    Sclabassi, Robert J.
    2007 IEEE 33RD ANNUAL NORTHEAST BIOENGINEERING CONFERENCE, 2007, : 112 - 113