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
Compiling for numa parallel machines
Compiling for numa parallel machines
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
Improving data locality with loop transformations
ACM Transactions on Programming Languages and Systems (TOPLAS)
Data-centric multi-level blocking
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
A compiler algorithm for optimizing locality in loop nests
ICS '97 Proceedings of the 11th international conference on Supercomputing
A hyperplane based approach for optimizing spatial locality in loop nests
ICS '98 Proceedings of the 12th international conference on Supercomputing
High Performance Compilers for Parallel Computing
High Performance Compilers for Parallel Computing
Compile-Time Techniques for Data Distribution in Distributed Memory Machines
IEEE Transactions on Parallel and Distributed Systems
A Matrix-Based Approach to the Global Locality Optimization Problem
PACT '98 Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques
On the Performance Enhancement of Paging Systems Through Program Analysis and Transformations
IEEE Transactions on Computers
A data layout optimization framework for NUCA-based multicores
Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture
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We present a cache locality optimization technique that can optimize a loop nest even if the arrays referenced have different layouts in memory. Such a capability is required for a global locality optimization framework that applies both loop and data transformations to a sequence of loop nests for optimizingl ocality. Our method finds a nonsingular iteration-space transformation matrix such that in a given loop nest spatial locality is exploited in the innermost loops where it is most useful. The method builds inverse of a non-singular transformation matrix column-by-column startingfrom the rightmost column. In addition, our approach can work in those cases where the data layouts of a subset of the referenced arrays is unknown. Experimental results on an 8-processor SGI Origin 2000 show that our technique reduces execution times by up to 72%.