PDEQSOL (Partial Differential Equation Solver Language) for Parallel Computers

  • Toshio Okochi


Several distributed-memory-type massively parallel computers have been developed and have proved to be efficient for numerical simulation of Partial Differential Equation (PDE) problems [1, 2, 3]. Although performance is high, it is very time-consuming to develop programs by using languages that have a special facility to control parallelism [4]. (In this chapter, “massively parallel computer ” means the distributed memory type.) On the other hand, in the automatic parallelization of general languages like FORTRAN and C, it is technically very difficult to achieve sufficient parallelizing efficiency for programs that are developed without special care for parallelization. The PDEQSOL (Partial Differential Equation Solver Language) system for massively parallel computers is being developed to overcome these limitations. PDEQSOL is a high-level programming language specially designed to describe PDE problems in a natural way for numerical analysis. The parallelizing translator generates a highly parallelized program for massively parallel computers by using the implicit parallelism in the PDEQSOL program.


Execution Time Parallel Computer Message Passing Space Domain Reference Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    K. Solchenbach, “Grid Applications on Distributed Memory Architectures: Implementation and Evaluation, ” Parallel Computing, vol.7, pp. 341–356, 1988.MATHCrossRefGoogle Scholar
  2. [2]
    K. Solchenbach, “Application Software for Suprenum, ” Supercomputer 30, pp. 44–50, March 1989.Google Scholar
  3. [3]
    E. R. Galea and C. S. Ierotheou, “A Parallel Implementation of a General Purpose Fluid Flow Code and its Application to Fire Field Modeling, ” Parallel Computing’ 91, pp. 601–608, 1991.Google Scholar
  4. [4]
    A.H. Karp and R. G. Babb II, “A Comparison of 12 Parallel FORTRAN Dialects, ” IEEE Software, September 1988, pp. 52–67.Google Scholar
  5. [5]
    Yukio Umetani and Michiru Tsuji and Kyouko Iwasawa and Hiroyuki Hi-rayama, “DEQSOL: A Mumerical Simulation Language for Vector/Parallel Processors, ” Proc. IFIP TC 2/W.G.2.5 Working Conferebce on Problem Solving Environments for Scientific Computing, Sophia Antipolis, France, 17–21 June, 1985, pp. 147–164, Amsterdam, North-Holland.Google Scholar
  6. [6]
    Chisato Konno, Miyuki Saji, Nobutoshi Sagawa, and Yukio Umetani, “Advanced Implicit Solution Function of DEQSOL and its Evaluation, ” Proc. Fall Joint Computer Conference, pp. 1026–1033, 1986.Google Scholar
  7. [7]
    Chisato Konno, Michiru Yamabe, Miyuki Saji, Nobutoshi Sagawa, Yukio Umetani, Hiroyuki Hirayama, and Tadashi Ohta, “ Automatic Code Genaration Method of DEQSOL (Differential Eqation Solver Language), ” Journal of Information Processing, Vol. 11, No. 1, pp. 15–21, 1987.Google Scholar
  8. [8]
    Hiroyuki Hirayama and Miiko Ikeda and Nobutoshi Sagawa, “Solution Function of PDEQSOL (Partial Differential EQuation SOlver Language) for Fluid Problems, ” Proc. Supercomputing’ 91, pp. 218–227, 1991.Google Scholar
  9. [9]
    H. P. Zima, H. Bast, M. Gerndt, “SUPERB: A Tool for Semi-automatic MIMD/SIMD Parallelization, ” Parallel Computing, vol.6, pp.1–18, 1986.CrossRefGoogle Scholar
  10. [10]
    S. Hiranandani, K. Kennedy, C. Teng, “Compiler Optimizations for FORTRAN D on MIMD Distributed-Memory Machines, ” Proc. Supercomputing’91, pp. 86–100, 1991.Google Scholar
  11. [11]
    M. Gupta and P. Banerjee, “Demonstration of Automatic Data Partitioning Techniques for Parallelizing Compilers on Multicomputers, ” IEEE Trans, on Parallel and Distributed Systems, Vol.3, No.2, pp.179–193, 1992.CrossRefGoogle Scholar
  12. [12]
    R. W. Hockney and E. A. Carmona, “Comparison of Communications on the Intel iPSC/860 and Touchstone Delta, ” Parallel Computing, vol.18, pp.1067–1072, 1992.MATHCrossRefGoogle Scholar
  13. [13]
    J. Li and M. Chen, “Compiling Communication-Efficient Programs for Massively Parallel Machines, ” IEEE Trans, on Parallel and Distributed Systems, Vol.2, No.3, pp.361–376, 1991.CrossRefGoogle Scholar
  14. [14]
    J. H. Saltz, R. Mirchandaney and K. Crowley, “Run-Time Parallelization and Scheduling of Loops, ” IEEE Trans, on Computers, Vol.40, No.5, pp.603–611, 1992.Google Scholar
  15. [15]
    C. Koelbel and P. Mehrotra, “Compiling Global Name-Space Parallel Loops for Distributed Execution, ” IEEE Trans, on Parallel and Distributed Systems, Vol.2, No.4, pp.440–451, 1992.Google Scholar
  16. [16]
    John R. Rice and Ronald F. Boisvert, “Solving Elliptic Problems Using ELLPACK, ” Springer-Verlag, New York, 1985.MATHCrossRefGoogle Scholar
  17. [17]
    E. N. Houstis and J. R. Rice, “Parallel ELLPACK:A Development and Problem Solving Environment for High Performance Computing Machines, ” Programming Environments for High-Level Scientific Problem Solving, pp. 229–243, Elsevier Science Publishers B.V., Amsterdam, North-Holland, 1992.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1995

Authors and Affiliations

  • Toshio Okochi
    • 1
  1. 1.Central Research LaboratoryHitachi Ltd.Kokubunji, Tokyo, 185Japan

Personalised recommendations