  We explore the sampling problem within the framework where parallel
evaluations of the gradient of the log-density are feasible. Our investigation
focuses on target distributions characterized by smooth and strongly
log-concave densities. We revisit the parallelized randomized midpoint method
and employ proof techniques recently developed for analyzing its purely
sequential version. Leveraging these techniques, we derive upper bounds on the
Wasserstein distance between the sampling and target densities. These bounds
quantify the runtime improvement achieved by utilizing parallel processing
units, which can be considerable.
