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The applicability of static data flow architectures to the iterative solution of sparse linear systems of equations is investigated. An analytic performance model of a static data flow computation is developed. This model includes both spatial parallelism, concurrent execution in multiple PEs, and pipelining, the streaming of data from array memories through the PEs. The performance model is used to analyze a row-partitioned iterative algorithms for solving sparse linear systems of algebraic equations. On the basis of this analysis, design parameters for the static data flow architecture as a function of matrix sparsity and dimension are proposed.