Endmember extraction (EE) is the first step in hyperspectral data unmixing. N-FINDR is one of the most commonly used EE algorithms. Nevertheless, its computational complexity is high, particularly, for a large data set. Following a parallel version of N-FINDR, i.e., P-FINDR, further improvements are presented in this paper. First, generic endmember re-extraction operation (GERO) and multiple search paths are introduced such that multiple endmembers are extracted in parallel. Second, by making full use of the advantages of the proposed algorithms, two extended schemes, i.e., extended mapping rule and multiple-stage GERO are presented, which can reduce synchronous cost and provide steady parallel performance. In experiments, the proposed algorithms have been quantitatively evaluated. The results demonstrate that they can outperform the conventional parallel computing and do not degrade the quality of EE.