Performance modeling of parallel Magnetostatic Wave calculations on shared memory multicore

The focus of this work is to identify data partitioning strategies and their performance models for memory intensive two dimensional Magneto-Static Wave (MSW) calculations for shared memory architecture. We have constructed computing, communication and synchronization time models for the different data partitioning schemes. We have identified that improved performance for any scheme can be achieved by reduced boundary sharing, decreasing stride penalties, reduced synchronization requirement and increased data sharing. A maximum speed-up of 3.9 for the largest data size is observed for one - dimensional partitioning.