  We propose a GPU-based distributed optimization algorithm, aimed at
controlling optimal power flow in multi-phase and unbalanced distribution
systems. Typically, conventional distributed optimization algorithms employed
in such scenarios rely on parallel execution with multiple CPUs. However, this
often leads to significant computation time primarily due to the need for
optimization solvers to solve subproblems for every iteration of the
algorithms. To address this computational challenge, we propose a distributed
optimization algorithm that eliminates solver dependencies and harnesses GPU
acceleration. The central idea involves decomposing networks to yield
subproblems with closed-form solutions based on matrix operations that GPU can
efficiently handle. We demonstrate the computational performance of our
approach through numerical experiments on four IEEE test instances ranging from
13 to 8500 buses. Our results affirm the scalability and superior speed of our
GPU-based approach compared to the CPU-based counterpart.
