Compiling parallel code for sparse matrix applications

Authors:
Vladimir Kotlyar;Keshav Pingali;Paul Stodghill
Affiliations:
Cornell University, Ithaca, NY;Cornell University, Ithaca, NY;Cornell University, Ithaca, NY
Venue:
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Year:
1997

Citing 14
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On the automatic parallelization of sparse and irregular Fortran programs[1]This work is supported in part by Army contract DABT63-95-C-0097; Army contract N66001-97-C-8532; NSF contract MIP-9619351; and a Partnership Award from IBM. This work is not necessarily representative of the positions or policies of the Army or Government.

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Abstract

We have developed a framework based on relational algebra for compiling efficient sparse matrix code from dense DO-ANY loops and a specification of the representation of the sparse matrix. In this paper, we show how this framework can be used to generate parallel code, and present experimental data that demonstrates that the code generated by our Bernoulli compiler achieves performance competitive with that of hand-written codes for important computational kernels.