RTGPU: Real-Time Computing with Graphics Processing Units

TL;DR

This paper surveys the use of GPUs in real-time systems, discussing their advantages, challenges like unpredictability, and existing solutions to improve their integration for time-critical applications.

Contribution

It provides a comprehensive overview of current challenges and solutions for integrating GPUs into real-time systems, highlighting open research directions.

Findings

GPU's parallel architecture accelerates complex tasks in real-time applications.

Challenges include non-preemptive execution and resource contention leading to delays.

Existing solutions involve scheduling, resource management, and synchronization techniques.

Abstract

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.