C is the lingua franca of programming and almost any device can be programmed using C. However, programming mod-ern heterogeneous architectures such as multi-core CPUs and GPUs requires explicitly expressing parallelism as well as device-specific properties such as memory hierarchies. The resulting code is often hard to understand, debug, and modify for different architectures. We propose to lift C programs to a parametric dataflow representation that lends itself to static data-centric analysis and enables automatic high-performance code generation. We separate writing code from optimizing for different hardware: simple, portable C source code is used to generate efficient specialized versions with a click of a button. Our approach can identify parallelism when no other compiler can, and outperforms a bespoke parallelized version of a scientific proxy application by up to 21%.
@article{arxiv.2112.11879,
title = {Lifting C Semantics for Dataflow Optimization},
author = {Alexandru Calotoiu and Tal Ben-Nun and Grzegorz Kwasniewski and Johannes de Fine Licht and Timo Schneider and Philipp Schaad and Torsten Hoefler},
journal= {arXiv preprint arXiv:2112.11879},
year = {2022}
}