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Avoiding Pitfalls when Using NVIDIA GPUs for Real-Time Tasks in Autonomous Systems

Authors Ming Yang, Nathan Otterness, Tanya Amert, Joshua Bakita, James H. Anderson, F. Donelson Smith

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Author Details

Ming Yang
  • The University of North Carolina at Chapel Hill, USA
Nathan Otterness
  • The University of North Carolina at Chapel Hill, USA
Tanya Amert
  • The University of North Carolina at Chapel Hill, USA
Joshua Bakita
  • The University of North Carolina at Chapel Hill, USA
James H. Anderson
  • The University of North Carolina at Chapel Hill, USA
F. Donelson Smith
  • The University of North Carolina at Chapel Hill, USA

Funding

Work supported by NSF grants CNS 1409175, CPS 1446631, CNS 1563845, and CNS 1717589, ARO grant W911NF-17-1-0294, and funding from General Motors.

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Ming Yang, Nathan Otterness, Tanya Amert, Joshua Bakita, James H. Anderson, and F. Donelson Smith. Avoiding Pitfalls when Using NVIDIA GPUs for Real-Time Tasks in Autonomous Systems. In 30th Euromicro Conference on Real-Time Systems (ECRTS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 106, pp. 20:1-20:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ECRTS.2018.20

Abstract

NVIDIA's CUDA API has enabled GPUs to be used as computing accelerators across a wide range of applications. This has resulted in performance gains in many application domains, but the underlying GPU hardware and software are subject to many non-obvious pitfalls. This is particularly problematic for safety-critical systems, where worst-case behaviors must be taken into account. While such behaviors were not a key concern for earlier CUDA users, the usage of GPUs in autonomous vehicles has taken CUDA programs out of the sole domain of computer-vision and machine-learning experts and into safety-critical processing pipelines. Certification is necessary in this new domain, which is problematic because GPU software may have been developed without any regard for worst-case behaviors. Pitfalls when using CUDA in real-time autonomous systems can result from the lack of specifics in official documentation, and developers of GPU software not being aware of the implications of their design choices with regards to real-time requirements. This paper focuses on the particular challenges facing the real-time community when utilizing CUDA-enabled GPUs for autonomous applications, and best practices for applying real-time safety-critical principles.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Heterogeneous (hybrid) systems
  • Computer systems organization → Embedded software
  • Computer systems organization → Real-time systems
  • Computer systems organization → Embedded and cyber-physical systems
  • Software and its engineering → Scheduling
  • Software and its engineering → Concurrency control
  • Software and its engineering → Process synchronization
Keywords
  • real-time systems
  • graphics processing units
  • scheduling algorithms
  • parallel computing
  • embedded software

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