Putting pointer analysis to work
POPL '98 Proceedings of the 25th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Vienna-Fortran/HPF Extensions for Sparse and Irregular Problems and Their Compilation
IEEE Transactions on Parallel and Distributed Systems
An efficient uniform run-time scheme for mixed regular-irregular applications
ICS '98 Proceedings of the 12th international conference on Supercomputing
Compiling parallel code for sparse matrix applications
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Distributed Memory Compiler Design For Sparse Problems
IEEE Transactions on Computers
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
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This work presents a novel strategy for the parallelization of applications containing sparse references. Our approach is a first step to converge from the data-parallel to the automatic parallelization by taking into account the semantical relationship of vectors composing a higher-level data structure. Applying a sparse privatization and a multiloops analysis at compile-time we enhance the performance and reduce the number of extra code annotations. The building/updating of a sparse matrix at run-time is also studied in this paper, solving the problem of using pointers and some levels of indirections on the left hand side. The evaluation of the strategy has been performed on a Cray T3E with the matrix transposition algorithm, using different temporary buffers for the sparse communication.