Evaluation of High-Level Programming Models for High-Performance Critical Systems

Abstract

Upcoming safety critical systems require high performance processing, which can be provided by multi-cores and embedded GPUs found in several Systems-on-chip (SoC) targeting these domains. So far, only low-level programming models and APIs, such as CUDA or OpenCL have been evaluated. In this Master thesis, we evaluate the effectiveness of higher level programming models, such as OpenACC and SYCL for critical applications executed in such embedded platforms. In particular, we are interested in two aspects: performance and programmability. In order to conduct our study, we use the GPU4S Bench benchmarking suite for space and a pedestrian detection application representing the automotive sector, which we port into the new programming models and analyze their behavior. We perform our evaluation on a representative embedded platform, the NVIDIA Xavier AGX which is considered a good candidate for future safety critical systems in both domains and compare our results with other programming models.