Abstract
The polyhedral model is now a well established and effective formalism for program optimization and parallelization, however, finding optimal transformations is a long-standing open problem and tools that allow practitioners to explore different choices through script-driven or user-guided transformations are needed. The polyhedral model is now a well established and effective formalism for program optimization and parallelization. However, finding optimal transformations is a long-standing open problem. It is therefore important to develop tools that, rather than following predefined optimization criteria, allow practitioners to explore different choices through script-driven or user-guided transformations. More than practitioners, such flexibility is even more important for compiler researchers and auto-tuner developers. In addition, tools must also raise the level of abstraction by representing and manipulating reductions and scans explicitly. And third, the tools must also be able to explore transformation choices that consider memory (re)-allocation.
