Automatic Stencil Code Generation- Ph.D. Thesis Proposal

Stencil-based kernels constitute the core of many scientific applications on block-structured grids. These calculations form the basis for a wide range of scientific applications from simple Jacobi iterations to complex multigrid and block structured adaptive PDE solvers. Unfortunately, these codes achieve a low fraction of peak performance, due primarily to the disparity between processor and main memory speeds. I propose for my Ph.D. dissertation research to develop an automatic system to generate highly efficient, platform-adapted implementations of stencil kernels. In practice, performance is a complex function of many factors, including compiler technology, machine architecture, instruction scheduling, and memory access behavior. However, through the use of performance models and search, we can generate very good, if not optimal stencil code. This tuned code can be over twice as fast as untuned code.