Maximum noise fraction (MNF) rotation is a classical method of hyperspectral image dimensionality reduction, and it needs a large amount of calculation and thus is time-consuming. This paper investigates the code transplantation and performance optimization for the maximum noise fraction algorithm on multi-core CPU and many integrated core (MIC) architecture. By analyzing hotspots of the MNF algorithm, parallel schemes are first designed for filtering, covariance matrix calculating and MNF transforming. Then, a series of optimization methods are presented and validated for various parallel schemes of different hotspots, including using math kernel library (MKL) functions. Finally, a C-MNF algorithm on multi-cores CPUs and an M-MNF algorithm on the CPU/MIC heterogeneous system are constructed. Experiments show that the C-MNF algorithm achieves impressive speedups (ranging from 58.9 to 106.4), and the M-MNF parallel algorithm runs the fastest, reaching a maximum speed-up of 137X.