Hierarchical Recursive Precision for Accelerating Symmetric Linear Solves
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Hierarchical Recursive Precision for Accelerating Symmetric Linear Solves on MXUs
Announce Type: replace Abstract: Symmetric positive-definite system solvers based on Cholesky factorization are fundamental to many scientific applications, such as climate modeling. We present a portable, nested recursive mixed-precision solver designed for Matrix Processing Units (MXUs), including NVIDIA Tensor Cores (H200) and AMD Matrix Cores (MI300X), that assigns low-precision FP16 arithmetic to large off-diagonal blocks, while preserving high precision on diagonal blocks to ensure...