Communication optimization and code generation for distributed memory machines
PLDI '93 Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation
Loop nest scheduling and transformations
Environments and tools for parallel scientific computing
Communication-free hyperplane partitioning of nested loops
Journal of Parallel and Distributed Computing
Improving locality and parallelism in nested loops
Improving locality and parallelism in nested loops
Some efficient solutions to the affine scheduling problem: I. One-dimensional time
International Journal of Parallel Programming
The Omega Library interface guide
The Omega Library interface guide
Maximizing parallelism and minimizing synchronization with affine transforms
Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Parallel algorithms in geometry
Handbook of discrete and computational geometry
An affine partitioning algorithm to maximize parallelism and minimize communication
ICS '99 Proceedings of the 13th international conference on Supercomputing
Generation of Efficient Nested Loops from Polyhedra
International Journal of Parallel Programming - Special issue on instruction-level parallelism and parallelizing compilation, part 2
Interoperability of Data Parallel Runtime Libraries
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
An Exact Method for Analysis of Value-based Array Data Dependences
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
Communication-Free Parallelization via Affine Transformations
LCPC '94 Proceedings of the 7th International Workshop on Languages and Compilers for Parallel Computing
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A technique, permitting automatic finding coarse grained parallelism in algorithms presented with arbitrary nested loops, is presented. The technique is based on finding affine space partition mappings. The main advantage of this technique is that it allows us to form constraints for finding mappings directly in a linear form while known techniques result in building non-linear constraints which should next be linearized. After finding affine space partition mappings, well-known code generation approaches can be applied to expose algorithm parallelism. It is shown how this technique can be applied for parallelizing computational geometry algorithms by means of two examples.