Directive-Based Compilers for GPUs

Abstract. General Purpose Graphics Computing Units can be effec-tively used for enhancing the performance of many contemporary scien-ti c applications. However, programming GPUs using machine-specic notations like CUDA or OpenCL can be complex and time consuming. In addition, the resulting programs are typically ne-tuned for a partic-ular target device. A promising alternative is to program in a conven-tional and machine-independent notation extended with directives and use compilers to generate GPU code automatically. These compilers en-able portability and increase programmer productivity and, if effective, would not impose much penalty on performance. This paper evaluates two such compilers, PGI and Cray. We rst iden-tify a collection of standard transformations that these compilers can apply. Then, we propose a sequence of manual transformations that pro-grammers can apply to enable the generation of efficient GPU kernels. Lastly, using the Rodinia Benchmark suite, we compare the performance of the code generated by the PGI and Cray compilers with that of code written in CUDA. Our evaluation shows that the code produced by the PGI and Cray compilers can perform well. For 6 of the 15 benchmarks that we evaluated, the compiler generated code achieved over 85 % of the performance of a hand-tuned CUDA version