A data locality optimizing algorithm
PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
Characterizing the behavior of sparse algorithms on caches
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
The SPARC architecture manual (version 9)
The SPARC architecture manual (version 9)
Fast parallel algorithms for short-range molecular dynamics
Journal of Computational Physics
Improving data locality with loop transformations
ACM Transactions on Programming Languages and Systems (TOPLAS)
A quantitative analysis of loop nest locality
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Block algorithms for sparse matrix computations on high performance workstations
ICS '96 Proceedings of the 10th international conference on Supercomputing
Computer architecture (2nd ed.): a quantitative approach
Computer architecture (2nd ed.): a quantitative approach
Improving performance of sparse matrix-vector multiplication
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Exact analysis of the cache behavior of nested loops
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Modeling data locality for the sparse matrix-vector product using distance measures
Parallel Computing - Linear systems and associated problems
Modeling and improving locality for the sparse-matrix-vector product on cache memories
Future Generation Computer Systems - I. High Performance Numerical Methods and Applications. II. Performance Data Mining: Automated Diagnosis, Adaption, and Optimization
Analytical Description of Locality for the Product of a Sparse Matrix by a Dense Matrix
PDPTA '02 Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications - Volume 1
Performance optimizations and bounds for sparse matrix-vector multiply
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Sparsity: Optimization Framework for Sparse Matrix Kernels
International Journal of High Performance Computing Applications
Optimizing sparse matrix-vector multiplication using index and value compression
Proceedings of the 5th conference on Computing frontiers
Performance evaluation of the sparse matrix-vector multiplication on modern architectures
The Journal of Supercomputing
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The combination of techniques based on reordering data with classic code restructuring techniques for increasing the locality in the execution of sparse algebra codes is studied in this paper. The reordering techniques are based on, first modeling the locality in run-time, and then applying a heuristic for increasing it. After this, a code restructuring technique specially tuned for sparse algebra codes called register blocking is applied. The product of a sparse matrix by a dense vector (SpM × V) is the code studied on different monoprocessors and distributed memory multiprocessors. The combination of both techniques was tested for a broad set of matrices from real problems and known repositories. The results expressed in terms of execution time show that an adequate reordering of the data improves the efficiency of applying register blocking, therefore, reducing the execution time for the sparse algebra code considered.