Theory of linear and integer programming
Theory of linear and integer programming
A data locality optimizing algorithm
PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
Unifying data and control transformations for distributed shared-memory machines
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Transformations of nested loops with non-convex iteration spaces
Parallel Computing
Improving Cache Locality by a Combination of Loop and Data Transformations
IEEE Transactions on Computers - Special issue on cache memory and related problems
Compiler Design Issues for Embedded Processors
IEEE Design & Test
Code Generation in the Polytope Model
PACT '98 Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques
Code Generation in the Polyhedral Model Is Easier Than You Think
Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques
Compilers: Principles, Techniques, and Tools (2nd Edition)
Compilers: Principles, Techniques, and Tools (2nd Edition)
A practical automatic polyhedral parallelizer and locality optimizer
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
| Hi-index | 0.00 |
The need for compilers of embedded systems to find effective ways of optimizing series of loop-nests is urgent. This is especially so for streaming applications such as M-Jpeg, H.264 etc. which are popular in embedded systems. The loop bounds and memory references of these applications are primarily affine functions of the outer loop counters and constant parameters. The polyhedral model provides powerful abstractions to optimize loop nests with such regular accesses. Affine transformations in this model capture a complex sequence of execution-reordering loop transformations. We propose a solution to the data locality optimization problem for the embedded systems by using the polyhedral model. Experiments show that our technique leads to 35 percent reduction in external memory accesses over best gcc optimization result.