Michelle Strout's Research Statement

loped one such run-time reordering technique called full sparse tiling. More importantly I have shown how the effect of sparse tiling and other run-time reordering techniques can be described at compile-time. Both contributions are discussed in greater detail below. Full Sparse Tiling for Gauss-Seidel I developed full sparse tiling [2] for Gauss-Seidel, a popular iterative solver for sparse systems of linear equations. Full sparse tiling performs a run-time reordering of computations across the GaussSeidel convergence iterations to improve inter-iteration data locality as well as improving intraiteration data locality and exploiting parallelism [3]. Experimental results indicate that parallelizing Gauss-Seidel with full sparse tiling can result in better performance than using an owner-computes strategy, which is the typical parallelization method used for irregular applications. Compile-Time Composition of Run-Time Data and Iteration Reorderings Run-time reorderings are implement