Enabling Polyhedral Optimizations in Julia

Julia is a dynamic programming language that, over the past few years, gained interest in the open-source community, especially in the field of scientific computing. Julia programs are executed by a virtual machine that translates the source code, during run-time, to machine code based on the LLVM compiler framework. LLVM provides a variety of analyses and transformation capabilities that are leveraged to optimize programs and facilitate efficient execution. More recently, LLVM was enhanced by a new optimization framework, namely Polly, that supports automatic parallelization and data-locality optimizations based on the polyhedral model. Polly is able to speed up compute kernels significantly, especially in the context of dense linear algebra and iterative stencil computations. In the course of this project I plan to integrate Polly into Julia to enable polyhedral optimizations for Julia programs.