Velociraptor: An embedded compiler toolkit for numerical programs targeting CPUs and GPUs

Abstract

Developing just-in-time (JIT) compilers that that allow scientific programmers to efficiently target both CPUs and GPUs is of increasing interest. However building such compilers requires considerable effort. We present a reusable and embeddable compiler toolkit called Velociraptor that can be used to easily build compilers for numerical programs targeting multicores and GPUs. Velociraptor provides a new high-level IR called VRIR which has been specifically designed for numeric computations, with rich support for arrays, plus support for highlevel parallel and GPU constructs. A compiler developer uses Velociraptor by generating VRIR for key parts of an input program. Velociraptor provides an optimizing compiler toolkit for generating CPU and GPU code and also provides a smart runtime system to manage the GPU. To demonstrate Velociraptor in action, we present two proof-of-concept case studies: a GPU extension for a JIT implementation of MATLAB language, and a JIT compiler for Python targeting CPUs and GPUs.