In this work, we survey the role of GPUs in real-time systems. Originally designed for parallel graphics workloads, GPUs are now widely used in time-critical applications such as machine learning, autonomous vehicles, and robotics due to their high computational throughput. Their parallel architecture is well-suited for accelerating complex tasks under strict timing constraints. However, their integration into real-time systems presents several challenges, including non-preemptive execution, execution time variability, and resource contention; factors that can lead to unpredictable delays and deadline violations. We examine existing solutions that address these challenges, including scheduling algorithms, resource management techniques, and synchronization methods, and highlight open research directions to improve GPU predictability and performance in real-time environments.

翻译：本文综述了GPU在实时系统中的作用。GPU最初为并行图形计算任务设计，如今因其高计算吞吐量，已广泛应用于机器学习、自动驾驶车辆和机器人等时间关键型应用。其并行架构非常适合在严格时间约束下加速复杂任务。然而，将GPU集成到实时系统中面临诸多挑战，包括非抢占式执行、执行时间可变性以及资源争用；这些因素可能导致不可预测的延迟和截止时间违反。我们分析了应对这些挑战的现有解决方案，包括调度算法、资源管理技术和同步方法，并指出了改善实时环境中GPU可预测性与性能的开放研究方向。