Sparse solvers provide essential functionality for a wide variety of scientific applications.
Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi …

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 …

This paper presents a unified communication optimization framework for sparse triangular
solve (SpTRSV) algorithms on CPU and GPU clusters. The framework builds upon a 3D …

We present the new features available in the recent release of SuperLU_DIST, Version 8.1.
1. SuperLU_DIST is a distributed-memory parallel sparse direct solver. The new features …

The sparse triangular solve (SpTRSV) kernel is an important building block for a number of
linear algebra routines such as sparse direct and iterative solvers. The major challenge of …

Modern high-performance computing systems have multiple GPUs and network interface
cards (NICs) per node. The resulting network architectures have multilevel hierarchies of …

Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based
HPC systems is a challenging task due to significant irregular memory references and …

In the last years, the portability term has enriched itself with new meanings: research
communities are talking about how to measure the degree to which an application (or …

We introduce a new parallel algorithm for solving sparse LU factorization of planar matrices,
which commonly arise in the finite element method for 2D PDEs. Existing scalable methods …

**aoye Sherry Li Refereed Journals 1. S. Jutamulia, G. Storti and X. Li, “Expert Systems
Based on LCTV AND/OR Logic” Optics Page 1 **aoye Sherry Li Last Update: 11/14/2020 …