Work-in-Progress: DAPHNE - An Automotive Benchmark Suite for Parallel Programming Models on Embedded Heterogeneous Platforms

Due to the ever-increasing computational demand of automotive applications, and in particular autonomous driving capabilities, the automotive industry and its suppliers are starting to adopt parallel and heterogeneous embedded computing platforms. However, C and C++, the currently dominating programming languages in this industry, do not provide sufficient mechanisms to fully exploit such platforms. As a result, vendors have begun to employ true parallel programming models such as OpenMP, CUDA or OpenCL. In this work, we report on a benchmark suite developed specifically to investigate the applicability of established parallel programming models to automotive workloads on heterogeneous platforms.

[1]  Shinpei Kato,et al.  An Open Approach to Autonomous Vehicles , 2015, IEEE Micro.

[2]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[3]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).