2014 23rd International Conference on Parallel Architecture and Compilation (PACT) (2014)
Aug. 23, 2014 to Aug. 27, 2014
Rahul Garg , School of Computer Science, McGill University, Montreal, Canada
Laurie Hendren , School of Computer Science, McGill University, Montreal, Canada
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.
Graphics processing units, Runtime, Arrays, MATLAB, Buildings, Data transfer,Python, GPU hybrid systems, Compiler framework for Array-Based Language, MATLAB
Rahul Garg, Laurie Hendren, "Velociraptor: An embedded compiler toolkit for numerical programs targeting CPUs and GPUs", 2014 23rd International Conference on Parallel Architecture and Compilation (PACT), vol. 00, no. , pp. 317-329, 2014, doi:10.1145/2628071.2628097