Lifting C Semantics for Dataflow Optimization

C is the lingua franca of programming and almost any device can be programmed using C. However, programming modern 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%.

Automated summary

The research proposes lifting C programs to a parametric dataflow representation for static data-centric analysis and automatic high-performance code generation. By separating writing code from optimizing for different hardware, efficient specialized versions can be generated from simple, portable C source code with the click of a button. This approach can identify parallelism and outperform existing compilers.