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In modern computers, a program's data locality can affect performance significantly. This paper details full sparse tiling, a run-time reordering transformation that improves the data locality for stationary iterative methods such as Gauss-Seidel operating on sparse matrices. In scientific applications such as finite element analysis, these iterative methods dominate the execution time. Full sparse tiling chooses a permutation of the rows and columns of the sparse matrix, and then an order of execution that achieves better data locality. We prove that full sparse-tiled Gauss-Seidel generates a solution that is bitwise identical to traditional Gauss-Seidel on the permuted matrix. We also present measurements of the performance improvements and the overheads of full sparse tiling and of cache blocking for irregular grids, a related technique developed by Douglas et al