|
SPONSORS
|
||||
|
|
COLLABORATIONS
|
||||||||||
|
Copyright Notice:
The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
Publications of SPCL
| A. Calotoiu, T. Ben-Nun, G. Kwasniewski, J. de Fine Licht, T. Schneider, P. Schaad, T. Hoefler: | ||
| Lifting C Semantics for Dataflow Optimization (In Proceedings of the 2022 International Conference on Supercomputing (ICS'22), Jul. 2022) AbstractC 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%Documentsdownload article: access preprint on arxiv:  download slides:  Recorded talk (best effort) | ||
BibTeX | ||
| ||