  The sequence-to-sequence (seq2seq) model for neural machine translation has
significantly improved the accuracy of language translation. There have been
new efforts to use this seq2seq model for program language translation or
program comparisons. In this work, we present the detailed steps of using a
seq2seq model to translate CUDA programs to OpenCL programs, which both have
very similar programming styles. Our work shows (i) a training input set
generation method, (ii) pre/post processing, and (iii) a case study using
Polybench-gpu-1.0, NVIDIA SDK, and Rodinia benchmarks.

Machine Learning;Programming Languages;Machine Learning