2 research outputs found
Efficient GPU implementation of a Boltzmann‑Schrödinger‑Poisson solver for the simulation of nanoscale DG MOSFETs
81–102, 2019) describes an efficient and accurate solver for nanoscale DG MOSFETs
through a deterministic Boltzmann-Schrödinger-Poisson model with seven
electron–phonon scattering mechanisms on a hybrid parallel CPU/GPU platform.
The transport computational phase, i.e. the time integration of the Boltzmann equations,
was ported to the GPU using CUDA extensions, but the computation of the
system’s eigenstates, i.e. the solution of the Schrödinger-Poisson block, was parallelized
only using OpenMP due to its complexity. This work fills the gap by describing
a port to GPU for the solver of the Schrödinger-Poisson block. This new proposal
implements on GPU a Scheduled Relaxation Jacobi method to solve the sparse linear
systems which arise in the 2D Poisson equation. The 1D Schrödinger equation
is solved on GPU by adapting a multi-section iteration and the Newton-Raphson
algorithm to approximate the energy levels, and the Inverse Power Iterative Method
is used to approximate the wave vectors. We want to stress that this solver for the
Schrödinger-Poisson block can be thought as a module independent of the transport
phase (Boltzmann) and can be used for solvers using different levels of description
for the electrons; therefore, it is of particular interest because it can be adapted to
other macroscopic, hence faster, solvers for confined devices exploited at industrial
level.Project PID2020-117846GB-I00 funded by the Spanish Ministerio de Ciencia
e InnovaciónProject A-TIC-344-UGR20 funded by European
Regional Development Fund