Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …

A power flow study aims to analyze a power system by obtaining the voltage and phase
angle of buses inside the power system. Power flow computation basically uses a numerical …

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 …

The sparse matrix solver by LU factorization is a serious bottleneck in Simulation Program
with Integrated Circuit Emphasis (SPICE)-based circuit simulators. The state-of-the-art …

Lower upper (LU) factorization for sparse matrices is the most important computing step for
circuit simulation problems. However, parallelizing LU factorization on the graphic …

Editor's note: Many scientific computing problems, including circuit simulations, rely on
efficient lower-upper (LU) decomposition of sparse matrices. Prior studies took advantage of …

Sparse LU factorization is one of the key building blocks of sparse direct solvers and often
dominates the computing time of circuit simulation programs. Existing GPU-accelerated …

In many power system applications, such as N–x static security analysis and Monte-Carlo-
simulation-based probabilistic power flow (PF) analysis, it is a very time-consuming task to …

LU factorization for sparse matrices is an important computing step for many engineering
and scientific problems such as circuit simulation. There have been many efforts toward …

High-performance general-purpose graphics processing units (GPGPUs) may suffer from
serious power and negative bias temperature instability (NBTI) problems. In this paper, we …