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Abstract
Memory-Centric X-ray Computational Tomography(CT is an iterative reconstruction technique that minimizes redundant computations with higher memory accesses. MemXCT implements a sparse matrix-vector multiplication(SpMV with multi-stage buffering and two-level pseudo-Hilbert ordering for optimization. Motivated by the need to validate conclusions from previous work, we reproduce the numerical results, the algorithms performance and the scaling behavior of the algorithms as the number of MPI processes increases on Azure.
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