Loop Optimization is important for high-performance computing but even more for fast image processing, machine learning, and accelerator programming. Over the last year the Polly loop optimization framework has significantly evolved, with new support for data-layout transformations, optimization of dense linear algebra kernels, and fully automatic accelerator mapping support. Many of these transformations have been contributed by developers all over the world, including three summer of code students. This BoF serves as a place for core developers to gather, to discuss the current status of Polly, and to shape the 2016 development agenda of Polly. Hot topics are likely new automatic GPGPU code generation facilities, recent improvements on correctness and compile time, the new outer loop vectorization, and the recent addition of @polly support in Julia. The Polly code base also relies heavily on scalar evolution, value range analysis, and can serve as a basis for performance, memory footprint, and data transfer modeling. We invite Polly developers and all other interested developers.
