The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: LLVM-HPC2023: The Ninth Workshop on the LLVM Compiler Infrastructure in HPC
Authors: Shilei Tian (Stony Brook University); Tom Scogland (Lawrence Livermore National Laboratory); Barbara Chapman (Stony Brook University, Hewlett Packard Enterprise (HPE)); and Johannes Doerfert (Lawrence Livermore National Laboratory)
Abstract: In this work, we introduce extensions to LLVM OpenMP, transforming it into a versatile and performance portable kernel language for GPU programming. These extensions allow for the seamless porting of programs written in kernel languages to high-performance OpenMP GPU programs with minimal modifications. To evaluate our extension, we implemented a proof-of-concept prototype that contains a subset of extensions we proposed. We ported six established CUDA proxy and benchmark applications and evaluated their performance on both AMD and NVIDIA platforms. By comparing with native versions (HIP and CUDA), our results demonstrate that OpenMP, augmented with our extensions, can not only match but also in some cases exceed the performance of kernel languages, thereby offering performance portability with minimal effort from application developers.
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