Abstract | Incremental Dynamic Analysis (IDA) is a novel procedure that has recently emerged to accurately estimate the seismic performance of structures using multiple nonlinear dynamic analyses under scaled ground motion records. Being a computer intensive method, IDA can benefit greatly by parallel execution. Our aim is to accelerate the computation of IDA analyses using realistic structural models and multiple ground motion records on commercial or academic analysis platforms that were designed to be run on a single processor. Taking advantage of an environment of multiple network-connected processors, it becomes possible to complete such difficult tasks “over the weekend”. Several approaches in distributing the computational load between the processors are discussed, examining the feasibility of breaking up tasks at the level of a model (sub-structuring), a single dynamic run, or a single-record IDA study. The latter two methods are the simplest to implement using a task-farming technique where a master processor prescribes tasks for the independent slave processors. It is shown that this approach can be efficiently implemented by modifying the IDA hunt&fill tracing algorithm to balance the computational load among a number of non-identical processors. The result is a flexible, efficient and fault-tolerant parallel platform with excellent scaling that can rapidly perform multiple multi-record IDA studies within the typical computer network found in any engineering office.