Characterizing the challenges and evaluating the efficacy of a CUDA-to-OpenCL translator

• Characterization of lexical, syntactic, and semantic mapping between CUDA and OpenCL.
• Overview of a prototype of an automated CUDA-to-OpenCL (i.e., CU2CL) translator.
• Evaluation of CU2CL, relative to coverage, translation time, and execution time.

The proliferation of heterogeneous computing systems has led to increased interest in parallel architectures and their associated programming models. One of the most promising models for heterogeneous computing is the accelerator model, and one of the most cost-effective, high-performance accelerators currently available is the general-purpose, graphics processing unit (GPU).Two similar programming environments have been proposed for GPUs: CUDA and OpenCL. While there are more lines of code already written in CUDA, OpenCL is an open standard that supports a broader. Hence, there is significant interest in automatic translation from CUDA to OpenCL.The contributions of this work are three-fold: (1) an extensive characterization of the subtle challenges of translation, (2) CU2CL (CUDA to OpenCL) — an implementation of a translator, and (3) an evaluation of CU2CL with respect to coverage of CUDA, translation performance, and performance of the translated applications.