The importance of considering system resiliency in the planning for water distribution system upgrades and rehabilitation is gaining increased attention by water utilities. To support analysis and decision making in this context, system planners and operators could benefit from modelling and data visualization tools that provide relevant information, such as resiliency maps that identify and prioritize critical watermains should they fail or otherwise be out of service. Although measuring the effects of an individual watermain failure and its impact on a system’s level of service (i.e., pressure, available fire flow, etc.) is a simple simulation process, to conduct a system-wide assessment and produce meaningful results requires multiple simulations and result data analytics which is increasingly cumbersome and computationally challenging as network sizes increase. This presentation will introduce a new tool that benefits from parallel processing to divide the computational load between available CPU cores and reduces the simulation time required to prioritize individual watermains based on their failure impact on the system. Using this approach allows the user to rank individual watermains based on the impact (e.g., number of nodes, total demands of nodes affected, etc.) as measured by a user-selected performance parameter (pressure drop, available fire flow reduction, etc.). The preliminary results indicate that this approach is able to rank all individual watermains in a moderate size network with 5,000 pipes in less than 10 minutes based on pressure drop criteria and less than 48 hours based on the available fire flow criteria.