Robot Operating System (ROS) promotes fault isolation, faster development, modularity, and core reusability and is therefore widely studied and used as the de facto standard for autonomous driving systems. Graphics processing units (GPUs) also facilitate high-performance computing and are therefore used for autonomous driving. As the requirements for real-time processing increase, methods for satisfying real-time constraints for ROS and GPUs are being developed. Unfortunately, scheduling algorithms specifying ROS's transportation (publish/subscribe) model, which can have execution order restrictions, are not being investigated, leading to the introduction of waiting time and degrading the responsiveness of the entire system. Furthermore, GPU tasks on ROS are also affected by the ROS transportation model, because central processing unit (CPU) time is occupied when GPU functions are launched. This paper proposes a loadable kernel module framework, called real-time ROS extension on transparent CPU/GPU coordination mechanism (ROSCH-G), for scheduling ROS in a heterogeneous environment without modifying the OS kernel and device drivers and then evaluates it experimentally. ROSCH-G provides a scheduling algorithm that considers ROS's execution order restrictions and a CPU/GPU coordination mechanism. Experimental results demonstrate that the proposed algorithm reduces the deadline miss rate and, compared with previous studies, makes effective use of the benefits of parallel processing. In addition, the results for the coordination mechanism demonstrate that ROSCH-G can schedule multiple GPU applications successfully.

Graphics processing unit (GPU) programming environments have matured for general-purpose computing on GPUs. Significant challenges for GPUs include system software support for bounded response times and guaranteed throughput. In recent years, GPU technologies have been applied to real-time systems by extending the operating system modules to support real-time GPU resource management. Unfortunately, such a system extension makes it difficult to maintain the system with version updates because the OS kernel and device drivers must be modified at the source-code level, thereby preventing continuous research and development of GPU technologies for real-time systems. A loadable kernel module (LKM) framework, called Linux Real-Time eXtention with GPUs (Linux-RTXG), for managing real-time GPU resources with Linux without modifying the OS kernel and device drivers is proposed and evaluated experimentally. Linux-RTXG provides mechanisms for interrupt interception and independent synchronization to achieve real-time scheduling and resource reservation capabilities for GPU applications on top of existing device drivers and runtime libraries. Experimental results demonstrate that the overhead incurred by introducing the proposed Linux-RTXG is comparable to that of introducing existing kernel-dependent approaches. In addition, the results demonstrate that multiple GPU applications can be scheduled successfully by Linux-RTXG to meet their priority and quality-of-service requirements in real time.