LAPACK: a portable linear algebra library for high-performance computers
Proceedings of the 1990 ACM/IEEE conference on Supercomputing
Exploiting task and data parallelism on a multicomputer
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
Automatic Extraction of Functional Parallelism from Ordinary Programs
IEEE Transactions on Parallel and Distributed Systems
FRONTIERS '95 Proceedings of the Fifth Symposium on the Frontiers of Massively Parallel Computation (Frontiers'95)
LAPACK Working Note 94: A User''s Guide to the BLACS v1.0
LAPACK Working Note 94: A User''s Guide to the BLACS v1.0
ICPP '94 Proceedings of the 1994 International Conference on Parallel Processing - Volume 02
A case for source-level transformations in MATLAB
Proceedings of the 2nd conference on Domain-specific languages
Exploiting task and data parallelism in parallel Hough and Radon transforms
ICPP '97 Proceedings of the international Conference on Parallel Processing
A MATLAB Compiler for Distributed, Heterogeneous, Reconfigurable Computing Systems
FCCM '00 Proceedings of the 2000 IEEE Symposium on Field-Programmable Custom Computing Machines
Match Virtual Machine: An Adaptive Runtime System to Execute MATLAB in Parallel
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Menhir: An environment for high performance Matlab
Scientific Programming
A case for source-level transformations in MATLAB
DSL'99 Proceedings of the 2nd conference on Conference on Domain-Specific Languages - Volume 2
ACM SIGAPL APL Quote Quad
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Automating GPU computing in MATLAB
Proceedings of the international conference on Supercomputing
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In this paper we suggest a new approach aimed at reducing the effort required to program distributed-memory multicomputers. The key idea in our approach is to automatically convert a program written in a library-based programming language (MATLAB) to a parallel program based on the ScaLAPACK parallel library. In the process of performing this conversion, we apply compiler optimizations that simultaneously exploit task and data parallelism. As our results show, our approach is feasible and practical and our optimization provides significant performance benefits.