Krylov subspace methods on supercomputers
SIAM Journal on Scientific and Statistical Computing
Global optimizations for parallelism and locality on scalable parallel machines
PLDI '93 Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
A course in computational algebraic number theory
A course in computational algebraic number theory
High Performance Compilers for Parallel Computing
High Performance Compilers for Parallel Computing
Communication-Free Hyperplane Partitioning of Nested Loops
Proceedings of the Fourth International Workshop on Languages and Compilers for Parallel Computing
The Alignment-Distribution Graph
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
| Hi-index | 0.00 |
Data and computation alignment is an important part of compiling sequential programs to architectures with non-uniform memory access times. In this paper, we show that elementary matrix methods can be used to determine communication-free alignment of code and data. We also solve the problem of replicatingd ata to eliminate communication. Our matrix-based approach leads to algorithms which work well for a variety of applications, and which are simpler and faster than other matrix-based algorithms in the literature.