Abstract
Multi-core CPUs with multiple levels of parallelism (i.e. data level, instruction level and task/core level) have become the mainstream CPUs for commodity computing systems. Based on the multi-core CPUs, in this paper we developed a high performance computing framework for AC contingency calculation (ACCC) to fully utilize the computing power of commodity systems for online and real time applications. Using Woodbury matrix identity based compensation method, we transform and pack multiple contingency cases of different outages into a fine grained vectorized data parallel programming model. We implement the data parallel programming model using SIMD instruction extension on x86 CPUs, therefore, fully taking advantages of the CPU core with SIMD floating point capability. We also implement a thread pool scheduler for ACCC on multi-core CPUs which automatically balances the computing loads across CPU cores to fully utilize the multi-core capability. We test the ACCC solver on the IEEE test systems and on the Polish 3000-bus system using a quad-core Intel Sandy Bridge CPU. The optimized ACCC solver achieves close to linear speedup (SIMD width multiply core numbers) comparing to scalar implementation and is able to solve a complete N-1 line outage AC contingency calculation of the Polish grid within one second on a commodity CPU. It enables the complete ACCC as a real-time application on commodity computing systems.