Work

Compiling parallel code for sparse matrix applications

Year: 1997

Type: conference-paper

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.

Source: ACM/IEEE Conference on Supercomputing (SC)

DOI: 10.1145/509593.509603

Authors: Vladimir Kotlyar, Keshav Pingali, Paul Stodghill

Institution: Cornell University

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W2159002770

FWCI: 1.407

Cites: 26

Cited by: 22

Related to: 10