Parallelizing of container terminal seaside layout optimizer using a Slurm cluster is considered in this paper. Maritime container terminals have quays divided into discrete berth segments. The number of berths and their lengths have to be adjusted to the arriving vessel traffic for high quality of service. A particular partition of the terminal quay into berths is called terminal layout. The vessel traffic is represented by the stochastic ship traffic model (STM). An instantiation of the STM is a specific ship arrival scenario. Evaluation of a quay layout on an arrival scenario requires scheduling vessels on the berths, which is a classic Berth Allocation Problem (BAP) of maritime logistics. The problem of partitioning terminal quay into berths, subject to vessel traffic, for high quality of service will be called a Stochastic Quay Partitioning Problem (SQPP). In this paper, we present a hill-climber metaheuristic for SQPP. The process of the partition evaluation is conducted on a Slurm cluster by a simple workflow. We report on the scalability and reliability of this workflow.