Abstract The authors present the parallel‐hybrid recursive least square (PH‐RLS) algorithm for an accurate self‐mixing interferometric laser vibration sensor coupled with an accelerometer under industrial conditions. Previously, this was achieved by using a conventional RLS algorithm to cancel the parasitic vibrations where the sensor itself is not in the stationary environment. This algorithm operates in sequential mode and due to its compute and data‐intensive nature, the algorithm does not work for real‐time applications, hence requires parallel computing. Therefore, the existing conventional RLS C program is parallelized by using hybrid OpenACe C/MPI (Open Accelerators/Message Passing Interface) parallel programming models and tested on Barcelona Supercomputing Center CTE‐Power9 Supercomputer. The computational performance of the proposed PH‐RLS algorithm is compared with the existing conventional RLS code by executing on multi distributed processors and uni‐core processor architecture, respectively. While comparing the performance of conventional RLS with a PH‐RLS algorithm on eight nodes of CTE‐Power9 supercomputer, the results show that the PH‐RLS algorithm gets 5857 times of performance improvement as compared to the conventional RLS implementation on a single node system. The results show that the proposed PH‐RLS also gives a scalable performance for a different range of vibration signals, making it a suitable choice for real‐time self‐mixing interferometer sensing systems working under industrial conditions.