Sparse direct solvers play a vital role in large-scale high performance computing in science
and engineering. Existing distributed sparse direct methods employ multifrontal/supernodal …

Optimizing circuit performance presents a pivotal challenge in the realm of automatic analog
physical design. The intricacy of analog performance arises from its sensitivity to layout …

Sparse general matrix-matrix multiplication (SpGEMM) is an important kernel in
computational science and engineering, and has been widely studied on homogeneous …

Conjugate gradient (CG) and biconjugate gradient stabilized (BiCGSTAB) are effective
methods used for solving sparse linear systems. We in this paper propose Mille-feuille, a …

Sparse triangular solve (SpTRSV) is one of the most important level-2 kernels in sparse
basic linear algebra subprograms (BLAS). Compared to another level-2 sparse BLAS kernel …

Sparse linear solvers play a crucial role in transistor-level circuit simulation, especially for
large-scale post-layout circuit simulation when considering complex parasitic effects. As …

Solving sparse linear systems dominates the simulation time for nonlinear integrated
circuits. Developing an effective preconditioner is crucial for accelerating the iterative solver …

Sparse LU factorization is the indispensable building block of the circuit simulation, and
dominates the simulation time, especially when dealing with large-scale circuits. Radio …

Dynamic LR and QR factorization are fundamental problems that exist widely in the control
field. However, the existing solutions under noises are lack of convergence speed and anti …

Sparse triangular solve (SpTRSV) is widely used in various domains. Numerous studies
have been conducted using CPUs, GPUs, and specific hardware accelerators, where …