GPU-Resident Sparse Direct Linear Solvers for Alternating Current Optimal Power Flow Analysis

TL;DR

This paper introduces a GPU-native sparse direct linear solver optimized for AC optimal power flow analysis, significantly accelerating computations and demonstrating the potential of GPU-based solutions for power system optimization.

Contribution

The paper presents the first GPU-native sparse direct solver compatible with AMD and NVIDIA GPUs, enhancing performance in ACOPF computations and exploring full GPU-based power system analysis.

Findings

Significant performance improvements with GPU-based solvers.

Feasibility of executing entire ACOPF analysis on GPU hardware.

Identification of research opportunities for heterogeneous systems.

Abstract

Integrating renewable resources within the transmission grid at a wide scale poses significant challenges for economic dispatch as it requires analysis with more optimization parameters, constraints, and sources of uncertainty. This motivates the investigation of more efficient computational methods, especially those for solving the underlying linear systems, which typically take more than half of the overall computation time. In this paper, we present our work on sparse linear solvers that take advantage of hardware accelerators, such as graphical processing units (GPUs), and improve the overall performance when used within economic dispatch computations. We treat the problems as sparse, which allows for faster execution but also makes the implementation of numerical methods more challenging. We present the first GPU-native sparse direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate significant performance improvements when using high-performance linear solvers within alternating current optimal power flow (ACOPF) analysis. Furthermore, we demonstrate the feasibility of getting significant performance improvements by executing the entire computation on GPU-based hardware. Finally, we identify outstanding research issues and opportunities for even better utilization of heterogeneous systems, including those equipped with GPUs.