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In linear solvers, like the conjugate gradient algorithm, sparse-matrix vector multiplication is an important kernel. Due to the sparseness of the matrices, the solver runs relatively slow. For digital optical tomography (DOT), a large set of linear equations have to be solved which currently takes in the order of hours on desktop computers. Our goal was to speed up the conjugate gradient solver. In this paper we present the results of applying multiple optimization techniques and exploitingdoi:10.1109/issoc.2007.4427436 dblp:conf/issoc/WiggersBKS07 fatcat:y7sfefqmc5dnjkbgqtp6q2a7ji