Multicore Acceleration of CG Algorithms Using Blocked-Pipeline-Matching Techniques

To realize the acceleration potential of multicore computing environments computational electromagnetics researchers must address parallel programming paradigms early in application development. We present a new blocked-pipeline-matched sparse representation and show speedup results for the conjugate gradient method by parallelizing the sparse matrix-vector multiplication kernel on multicore systems for a set of finite element matrices to demonstrate the potential of this approach. Performance of up to 8.2 GFLOPS was obtained for the proposed vectorized format using four Intel-cores, 17 × more than the nonvectorized version.