On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

Over the past decade, accelerator-based supercomputers have grown from 0% to 42% performance share on the TOP500. Ideally, GPU-accelerated code on such systems should be "write once, run anywhere," regardless of the GPU device (or for that matter, any parallel device, e.g., CPU or FPGA). In practice, however, portability can be significantly more limited due to the sheer volume of code implemented in non-portable languages. For example, the tremendous success of CUDA, as evidenced by the vast cornucopia of CUDA-accelerated applications, makes it infeasible to manually rewrite all these applications to achieve portability. Consequently, we achieve portability by using our automated CUDA-to-OpenCL source-to-source translator called CU2CL. To demonstrate the state of the practice, we use CU2CL to automatically translate three medium-to-large, CUDA-optimized codes to OpenCL, thus enabling the codes to run on other GPU-accelerated systems (as well as CPU-or FPGA-based systems). These automatically translated codes deliver performance portability, including as much as three-fold performance improvement, on a GPU device not supported by CUDA.