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Documents authored by Yang, Ming

Document
DOI: 10.4230/LIPIcs.ECRTS.2024.12
Autonomy Today: Many Delay-Prone Black Boxes

Authors: Sizhe Liu, Rohan Wagle, James H. Anderson, Ming Yang, Chi Zhang, and Yunhua Li

Published in: LIPIcs, Volume 298, 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)

Abstract
Machine-learning (ML) technology has been a key enabler in the push towards realizing ever more sophisticated autonomous-driving features. In deploying such technology, the automotive industry has relied heavily on using "black-box" software and hardware components that were originally intended for non-safety-critical contexts, without a full understanding of their real-time capabilities. A prime example of such a component is CUDA, which is fundamental to the acceleration of ML algorithms using NVIDIA GPUs. In this paper, evidence is presented demonstrating that CUDA can cause unbounded task delays. Such delays are the result of CUDA’s usage of synchronization mechanisms in the POSIX thread (pthread) library, so the latter is implicated as a delay-prone component as well. Such synchronization delays are shown to be the source of a system failure that occurred in an actual autonomous vehicle system during testing at WeRide. Motivated by these findings, a broader experimental study is presented that demonstrates several real-time deficiencies in CUDA, the glibc pthread library, Linux, and the POSIX interface of the safety-certified QNX Operating System for Safety. Partial mitigations for these deficiencies are presented and further actions are proposed for real-time researchers and developers to integrate more complete mitigations.
Cite as

Sizhe Liu, Rohan Wagle, James H. Anderson, Ming Yang, Chi Zhang, and Yunhua Li. Autonomy Today: Many Delay-Prone Black Boxes. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 12:1-12:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{liu_et_al:LIPIcs.ECRTS.2024.12,
  author =	{Liu, Sizhe and Wagle, Rohan and Anderson, James H. and Yang, Ming and Zhang, Chi and Li, Yunhua},
  title =	{{Autonomy Today: Many Delay-Prone Black Boxes}},
  booktitle =	{36th Euromicro Conference on Real-Time Systems (ECRTS 2024)},
  pages =	{12:1--12:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-324-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{298},
  editor =	{Pellizzoni, Rodolfo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2024.12},
  URN =		{urn:nbn:de:0030-drops-203152},
  doi =		{10.4230/LIPIcs.ECRTS.2024.12},
  annote =	{Keywords: autonomous driving, CUDA programming, locking protocols, POSIX thread, operating systems, machine learning systems, real-time systems}
}
Document
Artifact
DOI: 10.4230/DARTS.10.1.3
Autonomy Today: Many Delay-Prone Black Boxes (Artifact)

Authors: Sizhe Liu, Rohan Wagle, James H. Anderson, Ming Yang, Chi Zhang, and Yunhua Li

Published in: DARTS, Volume 10, Issue 1, Special Issue of the 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)

Abstract
Machine-learning (ML) technology has been a key enabler in the push towards realizing ever more sophisticated autonomous-driving features. In deploying such technology, the automotive industry has relied heavily on using "black-box" software and hardware components that were originally intended for non-safety-critical contexts, without a full understanding of their real-time capabilities. A prime example of such a component is CUDA, which is fundamental to the acceleration of ML algorithms using NVIDIA GPUs. In this paper, evidence is presented demonstrating that CUDA can cause unbounded task delays. Such delays are the result of CUDA’s usage of synchronization mechanisms in the POSIX thread (pthread) library, so the latter is implicated as a delay-prone component as well. Such synchronization delays are shown to be the source of a system failure that occurred in an actual autonomous vehicle system during testing at WeRide. Motivated by these findings, a broader experimental study is presented that demonstrates several real-time deficiencies in CUDA, the glibc pthread library, Linux, and the POSIX interface of the safety-certified QNX Operating System for Safety. Partial mitigations for these deficiencies are presented and further actions are proposed for real-time researchers and developers to integrate more complete mitigations.
Cite as

Sizhe Liu, Rohan Wagle, James H. Anderson, Ming Yang, Chi Zhang, and Yunhua Li. Autonomy Today: Many Delay-Prone Black Boxes (Artifact). In Special Issue of the 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Dagstuhl Artifacts Series (DARTS), Volume 10, Issue 1, pp. 3:1-3:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{liu_et_al:DARTS.10.1.3,
  author =	{Liu, Sizhe and Wagle, Rohan and Anderson, James H. and Yang, Ming and Zhang, Chi and Li, Yunhua},
  title =	{{Autonomy Today: Many Delay-Prone Black Boxes (Artifact)}},
  pages =	{3:1--3:3},
  journal =	{Dagstuhl Artifacts Series},
  ISBN =	{978-3-95977-327-0},
  ISSN =	{2509-8195},
  year =	{2024},
  volume =	{10},
  number =	{1},
  editor =	{Liu, Sizhe and Wagle, Rohan and Anderson, James H. and Yang, Ming and Zhang, Chi and Li, Yunhua},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.10.1.3},
  URN =		{urn:nbn:de:0030-drops-203259},
  doi =		{10.4230/DARTS.10.1.3},
  annote =	{Keywords: autonomous driving, CUDA programming, locking protocols, POSIX thread, operating systems, machine learning systems, real-time systems}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2018.20
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, and F. Donelson Smith

Published in: LIPIcs, Volume 106, 30th Euromicro Conference on Real-Time Systems (ECRTS 2018)

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.
Cite as

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)

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@InProceedings{yang_et_al:LIPIcs.ECRTS.2018.20,
  author =	{Yang, Ming and Otterness, Nathan and Amert, Tanya and Bakita, Joshua and Anderson, James H. and Smith, F. Donelson},
  title =	{{Avoiding Pitfalls when Using NVIDIA GPUs for Real-Time Tasks in Autonomous Systems}},
  booktitle =	{30th Euromicro Conference on Real-Time Systems (ECRTS 2018)},
  pages =	{20:1--20:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-075-0},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{106},
  editor =	{Altmeyer, Sebastian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2018.20},
  URN =		{urn:nbn:de:0030-drops-89845},
  doi =		{10.4230/LIPIcs.ECRTS.2018.20},
  annote =	{Keywords: real-time systems, graphics processing units, scheduling algorithms, parallel computing, embedded software}
}
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