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 …

Disaggregated memory is a promising approach that addresses the limitations of traditional
memory architectures by enabling memory to be decoupled from compute nodes and …

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 …

Large-scale sparse linear equation solver plays an important role in both numerical
simulation and artificial intelligence, and sparse triangular equation solver is a key step in …

Random walks on graphs are often used to analyse and predict epidemic spreads and to
investigate possible control actions to mitigate them. In this study, we first show that models …

Sparse triangular solve is used in conjunction with Sparse LU for solving sparse linear
systems, either as a direct solver or as a preconditioner. As GPUs have become a first-class …

Remote Memory Access (RMA) enables direct access to remote memory to achieve high
performance for HPC applications. However, most modern parallel programming models …

We discuss various models for epidemics on networks that rely on Markov chains. Random
walks on graphs are often used to predict epidemic spread and to investigate possible …

Tightly-coupled HPC systems have rigid memory allocation and can result in expensive
memory resource underutilization. As novel memory and network technologies mature …