High Performance Computing in Science and Engineering

Stencil calculations and matrix-free Krylov subspace solvers represent important components of many scientific computing applications. In these solvers, stencil applications are often the dominant part of the computation; an efficient parallel implementation of the kernel is therefore crucial to reduce the time to solution. Inspired by polynomial preconditioning, we remove upper bounds on the arithmetic intensity of the Krylov subspace building block by replacing the matrix with a higher-degree matrix polynomial. Using the latest state-of-the-art stencil compiler programs with temporal blocking, reduced memory bandwidth usage and, consequently, better utilization of SIMD vectorization and thus speedup on modern hardware, we are able to obtain performance improvements for higher polynomial degrees than simpler cache-blocking approaches have yielded in the past, demonstrating the new appeal of polynomial techniques on emerging architectures. We present results in a shared-memory environment and an extension to a distributed-memory environment with local shared memory.