Assessing and discovering parallelism in C $$++$$ + + code for heterogeneous platforms

Abstract

Massively parallel architectures are mainly based on a parallel heterogeneous setup. They are composed by different computing devices that speed up specific code regions, named kernels. These kernels are usually executed offline in the corresponding devices. Porting applications to a specific heterogeneous platform is a costly task in terms of time and human resources. The key points in the porting process are the manual analysis of the source code and kernel detection. Each device of these heterogeneous platforms has their own restrictions, such as the memory allocation support. Kernels must be mapped with suitable computing devices. We introduced AKI as an automatic kernel identification and annotation tool that aims to identify potential kernels on C $$++$$ sequential applications. AKI identifies those kernels that can be offlined on heterogeneous computing devices. To annotate these kernels, REPARA C++ attributes have been defined. This annotation mechanism can aid future automatic source-to-source transformation tools to facilitate the work for parallel heterogeneous platforms. AKI has been evaluated over all benchmarks included in the NAS suite. The benchmark suite incorporates a big set of realistic high performance applications. The evaluation results demonstrate that AKI is a competitive solution for identifying and annotating parallel code fragments (aka kernels).