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 paper, we evaluate the effectiveness of a higher level programming model, 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 open source GPU4S Bench benchmarking suite for space and an open source pedestrian detection application representing the automotive sector, which we port into SYCL and analyse their behavior. We perform our evaluation on a high-performance platform featuring an NVIDIA GTX 1080Ti as well as a representative embedded platform, the NVIDIA Xavier AGX which is considered a good candidate for future safety critical systems in both domains and we compare our results with other programming models. Our results show that in several cases SYCL is able to obtain performance close to highly optimised code using CUDA or NVIDIA libraries, with significantly lower development effort and complexity, which confirms the suitability of SYCL for programming high-performance safety critical systems.

This work was funded by the Ministerio de Ciencia e Innovacion - Agencia Estatal de Investigacion (PID2019-107255GB-C21 and IJC-2020-045931-I MCIN/AEI/10.13039/501100011033), the European Commission’s Horizon 2020 programme under the UP2DATE project (grant agreement 871465) and the HiPEAC Network of Excellence.

Peer Reviewed