High performance multi-GPU computing becomes an inevitable trend due to the ever-
increasing demand on computation capability in emerging domains such as deep learning …

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 …

As quantum computers evolve, simulations of quantum programs on classical computers will
be essential in validating quantum algorithms, understanding the effect of system noise, and …

High performance multi-GPU computing becomes an inevitable trend due to the ever-
increasing demand on computation capability in emerging domains such as deep learning …

Linear systems are occurring in many applications, and solving them can take a large
amount of the total simulation time. The high performance library hypre provides a variety of …

Sparse triangular solve (SpTRSV) is one of the most important kernels in many real-world
applications. Currently, much research on parallel SpTRSV focuses on level-set construction …

Segmented sort, as a generalization of classical sort, orders a batch of independent
segments in a whole array. Along with the wider adoption of manycore processors for HPC …

Integrating renewable resources within the transmission grid at a wide scale poses
significant challenges for economic dispatch as it requires analysis with more optimization …

The sparse triangular solve kernels, SpTRSV and SpTRSM, are important building blocks for
a number of numerical linear algebra routines. Parallelizing SpTRSV and SpTRSM on …

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 …