TY - GEN
T1 - Automatic parallelization of the AVL FIRE benchmark for a distributed-memory system
AU - Brezany, Peter
AU - Sipkova, Viera
AU - Chapman, Barbara
AU - Greimel, Robert
N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 1996.
PY - 1996
Y1 - 1996
N2 - Computational fluid dynamics (CFD) is a Grand Challenge discipline whose typical application areas, like aerospace and automotive engineering, often require enormous amount of computations. Parallel processing offers very high performance potential, but irregular problems like CFD have proven difficult to map onto parallel machines. In such codes, access patterns to major data arrays are dependent on some runtime data, therefore runtime preprocessing must be applied on critical code segments. So, automatic parallelization of irregular codes is a challenging problem. In this paper we describe parallelizing techniques we have developed for processing irregular codes that include irregularly distributed data structures. These techniques have been fully implemented within the Vienna Fortran Compilation System. We have examined the AVL FIRE benchmark solver GCCG, to evaluate the influence of different kinds of data distributions on parallel-program execution time. Experiments were performed using the Tjunc dataset on the iPSC/860.
AB - Computational fluid dynamics (CFD) is a Grand Challenge discipline whose typical application areas, like aerospace and automotive engineering, often require enormous amount of computations. Parallel processing offers very high performance potential, but irregular problems like CFD have proven difficult to map onto parallel machines. In such codes, access patterns to major data arrays are dependent on some runtime data, therefore runtime preprocessing must be applied on critical code segments. So, automatic parallelization of irregular codes is a challenging problem. In this paper we describe parallelizing techniques we have developed for processing irregular codes that include irregularly distributed data structures. These techniques have been fully implemented within the Vienna Fortran Compilation System. We have examined the AVL FIRE benchmark solver GCCG, to evaluate the influence of different kinds of data distributions on parallel-program execution time. Experiments were performed using the Tjunc dataset on the iPSC/860.
UR - https://www.scopus.com/pages/publications/84876919874
U2 - 10.1007/3-540-60902-4_7
DO - 10.1007/3-540-60902-4_7
M3 - Conference contribution
SN - 3540609024
SN - 9783540609025
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 50
EP - 60
BT - Applied Parallel Computing
A2 - Dongarra, Jack
A2 - Madsen, Kaj
A2 - Wasniewśki, Jerzy
PB - Springer Verlag
T2 - 2nd International Workshop on Applied Parallel Computing in Computations in Physics, Chemistry and Engineering Science, PARA 1995
Y2 - 21 August 1995 through 24 August 1995
ER -