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Transient stability constrained optimal power flow (TSCOPF) is able to reduce costs while keeping the operation point away from the stability boundary. While especially useful in modern power system operations, TSCOPF problems are practically very hard to solve; unacceptable computational time is considered to be one of the largest barriers in applying TSCOPF-based solutions. Based on the reduced-space interior point method (RIPM)-which takes advantage of the relatively few degrees of freedom and shows promising potential for solving large-scale TSCOPF problems-this paper introduces a parallel RIPM algorithm with high computing efficiency for multi-contingency TSCOPF problems. A two-level parallelism is developed to fully utilize the computing power of a Beowulf cluster equipped with multi-core CPUs. First, several compute-intensive steps of the TSCOPF algorithm are decomposed according to different contingencies with mathematical equivalent transformations, the corresponding computing tasks are assigned, stored, and processed on different nodes. Second, the distributed computing task is accelerated using elemental decomposition on Jacobian matrices, and then high performance multithreaded mathematical libraries are employed to fully exploit the multi-core CPUs on each node. The effectiveness of the proposed parallel algorithm is benchmarked on a Beowulf cluster with 16 computing nodes with 128 CPU cores using a series of test cases including up to 2746 buses and 16 contingencies. The results of the case studies indicate that the proposed parallel decomposition approach inherits the optimal solution and convergence properties of the original serial interior point method (IPM) approach and shows great capacity in accelerating TSCOPF solution.