  The use of Model Predictive Control in industry is steadily increasing as
more complicated problems can be addressed. Due to that online optimization is
usually performed, the main bottleneck with Model Predictive Control is the
relatively high computational complexity. Hence, a lot of research has been
performed to find efficient algorithms that solve the optimization problem. As
parallelism is becoming more commonly used in hardware, the demand for
efficient parallel solvers for Model Predictive Control has increased. In this
paper, a tailored parallel algorithm that can adopt different levels of
parallelism for solving the Newton step is presented. With sufficiently many
processing units, it is capable of reducing the computational growth to
logarithmic growth in the prediction horizon. Since the Newton step computation
is where most computational effort is spent in both interior-point and
active-set solvers, this new algorithm can significantly reduce the
computational complexity of highly relevant solvers for Model Predictive
Control.
