To enhance the computational efficiency of complete eigenvalue analysis in power system small-signal stability analysis

the parallelization of upper Hessenberg reduction algorithm in the QR method is studied. A block reduction algorithm is utilized to integrate the floating-point operations into high-level basic linear algebraic subprograms (BLAS). The block reduction algorithm is parallelized on hybrid CPU/GPU (graphic processing unit) system and applied to the complete eigenvalue analysis of large-scale power system small-signal stability analysis. Simulation results show that

compared with multi-core CPU parallelization

the GPU-based block upper Hessenberg reduction algorithm is able to obtain a speed-up ratio up to 5 times the original. The overall computing speed of the complete eigenvalue analysis

including the method proposed

has achieved remarkable acceleration improvement. The applicability of the QR method to large-scale power system simulation analysis is increased. This work is supported by State Grid Corporation of China

Major Projects on Planning and Operation Control of Large Scale Grid (No. SGCC-MPLG018-2012) and Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) of China (No. 20120073110020).