Iterative Refinement with Low-Precision Posits
<|reference_start|>Iterative Refinement with Low-Precision Posits: This research investigates using a mixed-precision iterative refinement method using posit numbers instead of the standard IEEE floating-point format. The method is applied to solve a general linear system represented by the equation $Ax = b$, where $A$ is a large sparse matrix. Various scaling techniques, such as row and column equilibration, map the matrix entries to higher-density regions of machine numbers before performing the $O(n^3)$ factorization operation. Low-precision LU factorization followed by forward/backward substitution provides an initial estimate. The results demonstrate that a 16-bit posit configuration combined with equilibration produces accuracy comparable to IEEE half-precision (fp16), indicating a potential for achieving a balance between efficiency and accuracy.<|reference_end|>
@article{quinlan2024iterative,
    title={Iterative Refinement with Low-Precision Posits},
    author={James Quinlan and E. Theodore L. Omtzigt},
    journal={arXiv preprint arXiv:2408.13400},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13400},
    primaryClass={math.NA cs.NA}
}