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 …

Power flow analysis is critical for power systems due to the development of multiple energy
supplies. For safety, stability, and real-time response in grid operation, grid planning, and …

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 …

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

Effective programming models offer programmers the ability to harness the capabilities of
the underlying platform. For decades, the two-sided Message Passing Interface (MPI) has …

[16] P.-A. Absil, R. Mahony, and B. Andrews. Convergence of the iterates of descent
methods for analytic cost functions. SIAM J. Optim., 16 (2): 531–547, 2005.[17] A. Abu-Omar …

In this report, we examine several emerging technologies of interest to the Department of
Energy and its computational centers. These include: 1) quantifying the benefit of tighter …

Power flow (PF) calculation is critical for power systems, as the development of multiple
energy supplies. Power system modeling and analysis have been challenging on power …