Putting Automatic Polyhedral Compilation for GPGPU to Work
Soufiane Baghdadi, Armin Größlinger, Albert Cohen · 2011
Abstract. Automatic parallelization is becoming more important as parallelism becomes ubiquitous. The first step for achieving automation is to develop a theoretical foundation, for example, the polyhedron model. The second step is to implement the algorithms studied in the theoretical framework and getting them to work in a compiler that can be used to parallelize real codes. The polyhedral model is a well-established theoretical foundation for parallelizing codes with static control. In this paper, we present, from a practical point of view, the challenges to solve for getting polyhedral compilation for GPUs to work. We choose the Polyhedral Compiler Collection (PoCC)as compiler infrastructureand target CUDAasthetarget platform; we plan to support OpenCL in the future. 1