This paper introduces a new efficient scalable parallel algorithm that computes all minimal transversals of a hypergraph. Finding all minimal transversals of a hypergraph is an important problem in data mining [1]. Previous work has focused primarily on efficient algorithms that run to completion. This paper extends an efficient algorithm to be parallel and scalable. Parallelism is achieved by duplicating an iterative state machine and splitting the workload. State of the art hypergraph transversal algorithms are compared and analyzed. Multiple hypergraph data sets are evaluated to demonstrate the various benefits and drawbacks of the various algorithms.