Translating CUDA to OpenCL for hardware generation using neural machine translation

Abstract

Hardware generation from high-level languages like C/C++ has been one of the dreams of software and hardware engineers for decades. Several high-level synthesis (HLS) or domain-specific languages (DSLs) have been developed to reduce the gap between high-level languages and hardware descriptive languages. However, each language tends to target some specific applications or there is a big learning curve in learning DSLs, which ends up having many program languages and tool chains. To address these challenges, we propose the use of a source-to-source translation to pick and choose which framework to use so that the hardware designer chooses the best target HLS/DSL that can be synthesized to the best performing hardware. In this work, we present source-to-source translation between CUDA to OpenCL using NMT, which we call PLNMT. The contribution of our work is that it develops techniques to generate training inputs. To generate a training dataset, we extract CUDA API usages from CUDA examples and write corresponding OpenCL API usages. With a pair of API usages acquired, we construct API usage trees that helps users find unseen usages from new samples and easily add them to a training input. Our initial results show that we can translate many applications from benchmarks such as CUDA SDK, polybench-gpu, and Rodinia. Furthermore, we show that translated kernel code from CUDA applications can be run in the OpenCL FPGA framework, which implies a new direction of HLS.