Interprocedural analyses of Fortran programs

Parallel Computing

Volumn 24, Issue 3-4, 1998, Pages 629-648

  Creusillet, B ^a   Irigoin, F ^a  

^a PSL RESEARCH UNIVERSITY   (France)

Author keywords

Interprocedural analysis; Parallelization; PIPS; Re engineering

Indexed keywords

COMPUTATIONAL LINGUISTICS; COMPUTER SYSTEMS PROGRAMMING; DATA REDUCTION; FLOWCHARTING; PARALLEL PROCESSING SYSTEMS; PROGRAM COMPILERS;

INTERPROCEDURAL ANALYSIS (IPA);

FORTRAN (PROGRAMMING LANGUAGE);

EID: 0032066709     PISSN: 01678191     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8191(98)00028-3     Document Type: Article

References (79)

1. W. Blume, R. Eigenmann, Performance analysis of parallelizing compilers on the Perfect Benchmarks programs, IEEE Trans. Parallel Distrib. Sys. 3 (6) (1992) 643-656.
2. P.M. Petersen, Evaluation of Programs and Parallelizing Compilers using Dynamic Analysis Techniques, PhD thesis, CSRD, University of Illinois, CSRD Report No. 1273, January 1993.
3. F.E. Allen, Interprocedural data flow analysis, In Proceedings of the IFIP Congress, 1974, pp. 398-402.
4. J. Loeliger, R. Metzger, Developing an interprocedural optimizing compiler, ACM SIGPLAN Notices 29 (4) (1994) 41-48.
5. V. Sarkar, PTRAN - the IBM parallel translation system, In Parallel Functional Programming Languages and Compilers, ACM Press Frontier Series, 1991, pp. 309-391.
6. D. Callahan, K. Cooper, R. Hood, K. Kennedy, L. Torczon, ParaScope: A parallel programming environment, Int. J. Supercomput. Appl. 2 (4) (1988) .
7. C. Polychronopoulos, M. Girkar, M. Haghighat, C. Lee, B. Leung, D. Schouten. Parafrase-2: an environment for parallelizing, partitioning, synchronizing and scheduling programs on multiprocessors, In International Conference on Parallel Processing, August 1989.
8. K. Smith, W. Appelbe, PAT - an interactive Fortran parallelizing assistant tool, Int. Conf. Parallel Processing 2 (1988) 58-62.
9. M. Hall, Managing Interprocedural Optimization. PhD thesis, Rice University, Houston, TX, April 1991.
10. D. Maydan, Accurate Analysis of Array References, PhD thesis, Stanford University, September 1992.
11. M. Burke, R. Cytron, Interprocedural dependence analysis and parallelization, In ACM SIGPLAN Symposium on Compiler Construction, 1986, pp. 162-175.
12. P. Carini, Automatic inlining, Research Report RC 10286, IBM, November 1995.
13. K. Cooper, M.W. Hall, K. Kennedy, Procedure cloning, In IEEE International Conference on Computer Language, April 1992.
14. E. Myers, A precise interprocedural data-flow algorithm, In Symposium on Principles of Programming Languages, January 1981, pp. 219-230.
15. J. Banning, An efficient way to find the side effects of procedure calls and the aliases of variables, In Symposium on Principles of Programming Languages, January 1979.
16. T. Marlowe, B. Ryder, M. Burke, Defining flow sensitivity in data-flow problems, Research Technical Report lcsr-tr-249, Rutgers University, Laboratory of Computer Science, July 1995.
17. J. Choi, R. Cytron, J. Ferrante, Automatic construction of sparse data flow evaluation graphs, In Symposium on Principles of Programming Languages, January 1991, pp. 55-66.
18. M. Hall, J. Mellor-Crummey, A. Carle, R. Rodríguez, FIAT: A framework for interprocedural analysis and transformation, In Sixth International Workshop on Languages and Compilers for Parallel Computing, August 1993.
19. K. Yi, W. Ludwell Harrison III, Automatic generation and management of interprocedural program analyses, In Symposium on Principles of Programming Languages, January 1993, pp. 246-259.
20. M.J. Harrold, B. Malloy, A unified interprocedural program representation for a maintenance environment, IEEE Tran. Software Eng. 19 (6) (1993) 584-593.
21. M. Hall, K. Kennedy, Efficient call graph analysis, Lett. Program. Languages Sys. 1 (3) (1992) 227-242.
22. B. Ryder, Constructing the call graph of a program, IEEE Trans. Software Eng. SE-5 (3) (1979) 216-225.
23. D. Callahan, The program summary graph and flow-sensitive interprocedural data-flow analysis, Int. Conf. Program. Language Design Implementation ACM SIGPLAN Notices 23 (7) (1988) 47-56.
24. D. Binkley, Interprocedural constant propagation using dependence graphs and a data-flow model, In International Conference on Compiler Construction, April 1994, pp. 374-388.
25. J. Ferrante, K. Ottenstein, J. Warren, The program dependence graph and its use in optimization, ACM Trans. Program. Languages Sys. 9 (3) (1987) 319-349.
26. B. Alpern, M.N. Wegman, F.K. Zadeck, Detecting equality of variables in programs, In Symposium on Principles of Programming Languages, January 1988, pp. 1-11.
27. R. Cytron, J. Ferrante, B. Rosen, M. Wegman, K. Zadeck, Efficiently computing static single assignment form and the control dependence graph, In Symposium on Principles of Programming Languages, January 1989, pp. 25-35.
28. E. Stoltz, M. Gelerk, M. Wolfe, Extended SSA with factored use-def chains to support optimization and parallelism, In International Conference on System Sciences, 1994, pp. 43-52.
29. E. Duesterwald, R. Gupta, M.L. Sofia, Demand-driven computation of interprocedural data-flow, In Symposium on Principles of Programming Languages, January 1995, pp. 37-48.
30. T. Reps, Solving demand versions of interprocedural analysis problems, In International Conference on Compiler Construction, April 1994, pp. 389-403.
31. M. Burke, An interval-based approach to exhaustive and incremental interprocedural data-flow analysis, ACM Trans. Program. Languages Sys. 12 (3) (1990) 341-395.
32. T. Marlowe, B. Ryder, An efficient hybrid algorithm for incremental data flow analysis, In Symposium on Principles of Programming Languages, January 1990, pp. 184-196.
33. V. Sreedhar, G. Gao, Y.-F. Lee, A new framework for exhaustive and incremental analysis using DJ graphs, In International Conference on Programming Language Design and Implementation, May 1996, pp. 278-290.
34. R. Gupta, L. Pollock, M.L. Soffa, Parallelizing data-flow analysis, In Workshop on Parallel Compilation, May 1990.
35. Y.-F. Lee, B. Ryder, M. Fiuczynski, Region analysis: a parallel elimination method for data flow analysis, IEEE Tran. Software Eng. 21 (11) (1995) 913-926.
36. W. Landi, B. Ryder, A safe approximation algorithm for interprocedural pointer aliasing, In International Conference on Programming Language Design and Implementation, 1992, pp. 235-248.
37. T. Reps, S. Horwitz and M. Sagiv. Precise interprocedural dataflow analysis via graph reachability, In Symposium on Principles of Programming Languages, January 1995, pp. 49-61.
38. M. Sharir, A. Pneulli. Two approaches to interprocedural data-flow analysis. In: S. Muchnick, N. Jones (Eds.), Program Flow Analysis, Prentice-Hall, 1981, pp. 189-233.
39. K.D. Cooper, K. Kennedy, Fast interprocedural alias analysis, In Symposium on Principles of Programming Languages, February 1989, pp. 49-59.
40. H. Mayer, M. Wolfe, Interprocedural alias analysis: implementation and empirical results, Software Practice Experience 23 (11) (1993) 1201-1233.
41. K.D. Cooper, K. Kennedy, Efficient computation of flow insensitive interprocedural summary information, In ACM SIGPLAN Symposium on Compiler Construction, June 1984, pp. 247-258.
42. E. Morel, C. Renvoise, Interprocedural elimination of partial redundancies, In: S. Muchnick, N. Jones (Eds.), Program Flow Analysis, Prentice-Hall, 1981.
43. C. Callahan, K. Cooper, K. Kennedy, L. Torczon, Interprocedural constant propagation, In ACM SIGPLAN Symposium on Compiler Construction, 1986, pp. 152-161.
44. P. Carini, Flow-sensitive interprocedural constant propagation, Research Report RC 20290, IBM, November 1995.
45. D. Grove, L. Torczon, Interprocedural constant propagation: A study of jump functions implementations, In International Conference on Programming Language Design and Implementation, ACM SIGPLAN Notices, June 1993, pp. 90-99.
46. M. Haghighat, Symbolic Analysis for Parallelizing Compilers, Kluwer Academic Publishers, 1995.
47. M. Hall, B. Murphy, S. Amarasinghe, S.-W. Liao, M. Lam, Interprocedural analysis for parallelization, In Languages and Compilers for Parallel Computing, Lecture Notes in Computer Science Springer-Verlag, August 1995, pp. 61-80.
48. W. Blume, R. Eigenmann, The range test: A dependence test for symbolic, nonlinear expressions, In International Conference on Supercomputing, 1994, pp. 528-537.
49. F. Irigoin, Interprocedural analyses for programming environments, In Workshop on Environments and Tools for Parallel Scientific Computing, North-Holland, September 1992, pp. 333-350.
50. F. Irigoin, P. Jouvelot, R. Triolet, Semantical interprocedural parallelization: An overview of the PIPS project, In International Conference on Supercomputing, June 1991, pp. 144-151.
51. R. Triolet, P. Feautrier, F. Irigoin, Direct parallelization of call statements, In ACM SIGPLAN Symposium on Compiler Construction, July 1986, pp. 176-185.
52. V. Balasundaram, K. Kennedy, A technique for summarizing data access and its use in parallelism enhancing transformations, In International Conference on Programming Language Design and Implementation, June 1989, pp. 41-53.
53. D. Callahan, K. Kennedy, Analysis of interprocedural side effects in a parallel programming environment, J. Parallel Distrib. Comput. 5 (10) (1988) 517-550.
54. P. Havlak, K. Kennedy, An implementation of interprocedural bounded regular section analysis, IEEE Tran. Parallel Distrib. Sys. 2 (3) (1991) 350-360.
55. Z. Li, P.-C. Yew. Efficient interprocedural analysis and program parallelization and restructuring, In Symposium on Parallel Processing: Experience with Applications, Languages and Systems, 1988, pp. 85-99.
56. S. Johnson, M. Cross, M. Everett, Exploitation of symbolic information in interprocedural dependence analysis, Parallel Comput. 22 (1996) 197-226.
57. B. Creusillet, F. Irigoin, Interprocedural array region analyses, Int. J. Parallel Program. 24 (6) (1996) 513-546, (special issue on LCPC).
58. J. Gu, Z. Li, G. Lee, Symbolic array data-flow analysis for array privatization and program parallelization, In Supercomputing, December 1995.
59. M. Hall, S. Amarasinghe, B. Murphy, S.-W. Liao, M. Lam, Detecting coarse-grain parallelism using an interprocedural parallelizing compiler, In Supercomputing, December 1995.
60. P. Tu, Automatic Array Privatization and Demand-Driven Symbolic Analysis, PhD thesis, University of Illinois at Urbana-Champaign, 1995.
61. G. Agrawal, J. Saltz, Interprocedural partial redundancy elimination and its application to distributed memory compilation, In International Conference on Programming Language Design and Implementation, June 1995, pp. 258-269.
62. S. Benkner, H.P. Brezany, P. Zima, Processing array statements and procedure interfaces in the PREPARE High Performance Fortran compiler, In International Conference on Compiler Construction, Lecture Notes in Computer Science, April 1994, pp. 324-338.
63. M. Hall, S. Hiranandani, K. Kennedy, C.-W. Tseng, Interprocedural compilation of Fortran D, J. Parallel Distrib. Comput. 38 (2) (1996) 114-129.
64. W. Blume, R. Doallo, R. Eigenmann, J. Grout, J. Hoeflinger, T. Lawrence, J. Lee, D. Padua, Y. Paek, B. Pottenger, L. Rauchwerger, P. Tu, Parallel programming with Polaris, Computer 29 (12) (1996) 78-82.
65. M. Hall, J. Anderson, S. Amarasinghe, B. Murphy, S.-W. Liao, E. Bugnion, M. Lam, Maximizing multiprocessor performance with the SUIF compiler, Computer 29 (12) (1996) 84-89.
66. M. Burke, L. Torczon, Interprocedural optimization: eliminating unnecessary recompilation, ACM Trans. Program. Languages Sys. 15 (3) (1993) 367-399.
67. American National Standard Institute, Programming Language FORTRAN, ANSI X3.9-1978, ISO 1539-1980, 1983.
68. B. Creusillet, Array Region Analyses and Applications, PhD thesis, École des mines de Paris, Available at http://www.cri.ensmp.fr/doc/A-295.ps.gz., December 1996.
69. F. Coelho, Compilation of I/O communications for HPF, In Frontiers'95, available via http://www.cri.ensmp.fr/~coelho, February 1995, pp. 102-109.
70. A. Platonoff, Automatic data distribution for massively parallel computers, In Fifth International Workshop on Compilers for Parallel Computers, June 1995, pp. 555-570.
71. C. Ancourt, F. Coelho, B. Creusillet, R. Keryell, How to add a new phase in PIPS: The case of dead code elimination, In Sixth International Workshop on Compilers for Parallel Computers, December 1996, pp. 19-30.
72. W. Blume, R. Eigenmann, Symbolic analysis techniques needed for the effective parallelization of the Perfect Benchmarks, In International Conference on Parallel Processing, 1994.
73. K. Cooper, M. Hall, R. Hood, K. Kennedy, K. McKinley, J. Mellor-Crummey, L. Torczon, S. Waren, The Parascope parallel programming environment, Proc. IEEE 81 (2) (1993) .
74. J. Gu, Z. Li, G. Lee, Experience with efficient data flow analysis for array privatization, In Symposium on Principles and Practice of Parallel Programming, June 1997, pp. 157-167.
75. T. Nguyen, J. Gu, Z. Li, An interprocedural parallelizing compiler and its support for memory hierarchy research, In Languages and Compilers for Parallel Computing, Lecture Notes in Computer Science, Springer-Verlag, August 1995, pp. 96-110.
76. M.J. Harrold, M.L. Soffa, Efficient computation of interprocedural definition-use chains, ACM Tran. Program. Languages Sys. 16 (2) (1994) 175-204.
77. S. Graham, S. Lucco, O. Sharp, Orchestrating interactions among parallel computations, In International Conference on Programming Language Design and Implementation, ACM SIGPLAN Notices, June 1993, pp. 100-111.
78. M. Hind, M. Burke, P. Carini, S. Midkiff, Interprocedural array analysis: How much precision do we need? In Third Workshop on Compilers for Parallel Computers, July 1992, pp. 48-64.
79. M. Alt, F. Martin, Generation of efficient interprocedural analyzers with PAG, In Static Analysis Symposium, Lecture Notes in Computer Science, Springer-Verlag, 1995, pp. 33-50.