  Motion planning and control in autonomous car racing are one of the most
challenging and safety-critical tasks due to high speed and dynamism. The
lower-level control nodes are expected to be highly optimized due to resource
constraints of onboard embedded processing units, although there are strict
latency requirements. Some of these guarantees can be provided at the
application level, such as using ROS2's Real-Time executors. However, the
performance can be far from satisfactory as many modern control algorithms
(such as Model Predictive Control) rely on solving complicated online
optimization problems at each iteration. In this paper, we present a simple yet
effective multi-threading technique to optimize the throughput of
online-control algorithms for resource-constrained autonomous racing platforms.
We achieve this by maintaining a systematic pool of worker threads solving the
optimization problem in parallel which can improve the system performance by
reducing latency between control input commands. We further demonstrate the
effectiveness of our method using the Model Predictive Contouring Control
(MPCC) algorithm running on Nvidia's Xavier AGX platform.
