Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Advanced compiler optimizations for supercomputers
Communications of the ACM - Special issue on parallelism
Direct methods for sparse matrices
Direct methods for sparse matrices
Run-Time Parallelization and Scheduling of Loops
IEEE Transactions on Computers
ICS '89 Proceedings of the 3rd international conference on Supercomputing
Optimizing Supercompilers for Supercomputers
Optimizing Supercompilers for Supercomputers
Parallel Programming and Compilers
Parallel Programming and Compilers
Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
Solving Linear Systems on Vector and Shared Memory Computers
Solving Linear Systems on Vector and Shared Memory Computers
Advanced compiler optimizations for sparse computations
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
ICS '94 Proceedings of the 8th international conference on Supercomputing
Automatic Data Structure Selection and Transformation for Sparse Matrix Computations
IEEE Transactions on Parallel and Distributed Systems
Next-generation generic programming and its application to sparse matrix computations
Proceedings of the 14th international conference on Supercomputing
A framework for sparse matrix code synthesis from high-level specifications
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Array language support for parallel sparse computation
ICS '01 Proceedings of the 15th international conference on Supercomputing
Compiler supported high-level abstractions for sparse disk-resident datasets
ICS '02 Proceedings of the 16th international conference on Supercomputing
SIPR: A New Framework for Generating Efficient Code for Sparse Matrix Computations
LCPC '98 Proceedings of the 11th International Workshop on Languages and Compilers for Parallel Computing
Data Centric Transformations on Non-Integer Iteration Spaces
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
Pattern-based sparse matrix representation for memory-efficient SMVM kernels
Proceedings of the 23rd international conference on Supercomputing
LCPC'04 Proceedings of the 17th international conference on Languages and Compilers for High Performance Computing
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The problem of compiler optimization of sparse codes is well known and no satisfactory solutions have been found yet. One of the major obstacles is formed by the fact that sparse programs deal explicitly with the particular data structures selected for storing sparse matrices. This explicit data structure handling obscures the functionality of a code to such a degree that the optimization of the code is prohibited, e.g. by the introduction of indirect addressing. The method presented in this paper postpones data structure selection until the compile phase, thereby allowing the compiler to combine code optimization with explicit data structure selection. Not only enables this method the compiler to generate efficient code for sparse computations, also the task of the programmer is greatly reduced in complexity.