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Aztec is a parallel iterative library for solving linear systems which is both easy-to-use and efficient. Simplicity is attained using the notion of a global distributed matrix. The global distributed matrix allows a user to specify pieces (different rows for different processors) of his application matrix exactly as he would in the serial setting (i.e. using a global numbering scheme). Issues such as local numbering, ghost variables, and messages are ignored by the user and are instead computed by an automated transformation function. Efficiency is achieved using standard distributed memory techniques; locally numbered submatrices, ghost variables, and message information computed by the transformation function are maintained by each processor so that local calculations and communication of data dependencies is fast. Additionally, Aztec takes advantage of advanced partitioning techniques (Chaco) and utilizes efficient dense matrix algorithms when solving block sparse matrices.

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