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Spectral unmixing is an important task for hyperspectral data exploitation. It generally consists of two steps: identification of pure spectral signatures (endmembers) and estimation of the fractional abundance of each endmember in each pixel of the scene. A successful algorithm to perform both tasks in simultaneous fashion is the iterative error analysis (IEA) algorithm, which applies an iterative process in which the next endmember to be detected depends on the set of previously extracted ones, which can be computationally expensive for hyperspectral images with a large number of endmembers. In this paper, we propose a new parallel implementation of the IEA algorithm for graphics processing units (GPUs). The proposed implementation is tested on three different GPUs fromNVidia™, and is shown to exhibit real-time performance in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data set collected over the Cuprite mining district in Nevada.