Automatic data structure selection and transformation for sparse matrix computations

被引:22
|
作者
Bik, AJC
Wijshoff, HAG
机构
[1] High Performance Computing Division, Dept. of Computer Science, Leiden University
关键词
data structure selection; data structure transformations; restructuring compilers; sparse matrix computations; program transformations; LINEAR ALGEBRA SUBPROGRAMS;
D O I
10.1109/71.485501
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The problem of compiler optimization of sparse codes is well known and no satisfactory solutions have been found yet. One of the major obstacles is formed by the fact that sparse programs explicitly deal with particular data structures selected for storing sparse matrices. This explicit data structure handling obscures the functionality of a code to such a degree that optimization of the code is prohibited, for instance, by the introduction of indirect addressing. The method presented in this paper delays data structure selection until the compile phase, thereby allowing the compiler to combine code optimization with explicit data structure selection. This method enables the compiler to generate efficient code for sparse computations. Moreover, the task of the programmer is greatly reduced in complexity.
引用
收藏
页码:109 / 126
页数:18
相关论文
共 50 条
  • [41] Parallel sparse matrix computations in the industrial strength PINEAPL library
    Krommer, AR
    APPLIED PARALLEL COMPUTING: LARGE SCALE SCIENTIFIC AND INDUSTRIAL PROBLEMS, 1998, 1541 : 281 - 285
  • [42] BASIC SPARSE-MATRIX COMPUTATIONS ON THE CM-5
    PETITON, S
    SAAD, Y
    WU, KS
    FERNG, W
    INTERNATIONAL JOURNAL OF MODERN PHYSICS C-PHYSICS AND COMPUTERS, 1993, 4 (01): : 65 - 83
  • [43] Segmented Merge: A New Primitive for Parallel Sparse Matrix Computations
    Haonan Ji
    Shibo Lu
    Kaixi Hou
    Hao Wang
    Zhou Jin
    Weifeng Liu
    Brian Vinter
    International Journal of Parallel Programming, 2021, 49 : 732 - 744
  • [44] Vectorizing Sparse Matrix Computations with Partially-Strided Codelets
    Cheshmi, Kazem
    Cetinic, Zachary
    Dehnavi, Maryam Mehri
    SC22: INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, 2022,
  • [45] Heterogeneous Sparse Matrix Computations on Hybrid GPU/CPU Platforms
    Cardellini, Valeria
    Fanfarillo, Alessandro
    Filippone, Salvatore
    PARALLEL COMPUTING: ACCELERATING COMPUTATIONAL SCIENCE AND ENGINEERING (CSE), 2014, 25 : 203 - 212
  • [46] Runtime sparse matrix format selection
    Armstrong, Warren
    Rendell, Alistair P.
    ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS, 2010, 1 (01): : 135 - 144
  • [47] Multimedia representation of matrix computations and data
    Vazhenin, A
    Mirenkov, N
    Vazhenin, D
    PROCEEDINGS OF THE FIFTH JOINT CONFERENCE ON INFORMATION SCIENCES, VOLS 1 AND 2, 2000, : A592 - A595
  • [48] Multimedia representation of matrix computations and data
    Vazhenin, AP
    Mirenkov, NN
    Vazhenin, DA
    INFORMATION SCIENCES, 2002, 141 (1-2) : 97 - 122
  • [49] Automatic Transformation from Data Flow Diagram to Structure Chart
    Software Engineering Notes, 22 (04):
  • [50] AUTOMATIC SELECTION OF SPEAKERS FOR IMPROVED ACOUSTIC MODELLING: RECOGNITION OF DISORDERED SPEECH WITH SPARSE DATA
    Christensen, H.
    Casanueva, I.
    Cunningham, S.
    Green, P.
    Hain, T.
    2014 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY SLT 2014, 2014, : 254 - 259