关键词：LAPACK；ASD；稀疏块
摘 要：The goal of this thesis is to allow for automatic sparsity detection (ASD) within LAPACK that is completely hidden from the user and provides no slowdown for users running fully dense matrices. This work adds modular support for the detection of blocked sparsity within LAPACK LU and Cholesky functions. It also creates the infrastructure and the algorithms to potentially expand sparsity detection to other factorizations, more input matrix structures, or provide further timing and memory improvements via integration directly in the solver routines. Two general approaches are implemented named `Profile' (ASD1) and `Sparse block' (ASD2) with a third more complicated method named `Full sparsity' (ASD3) being described more abstractly, only at an algorithm level.