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DOI: 10.4230/LIPIcs.ECRTS.2020.10

AMD GPUs as an Alternative to NVIDIA for Supporting Real-Time Workloads

Authors: Nathan Otterness and James H. Anderson

Published in: LIPIcs, Volume 165, 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)

Abstract

Graphics processing units (GPUs) manufactured by NVIDIA continue to dominate many fields of research, including real-time GPU-management. NVIDIA’s status as a key enabling technology for deep learning and image processing makes this unsurprising, especially when combined with the company’s push into embedded, safety-critical domains like autonomous driving. NVIDIA’s primary competitor, AMD, has received comparatively little attention, due in part to few embedded offerings and a lack of support from popular deep-learning toolkits. Recently, however, AMD’s ROCm (Radeon Open Compute) software platform was made available to address at least the second of these two issues, but is ROCm worth the attention of safety-critical software developers? In order to answer this question, this paper explores the features and pitfalls of AMD GPUs, focusing on contrasting details with NVIDIA’s GPU hardware and software. We argue that an open software stack such as ROCm may be able to provide much-needed flexibility and reproducibility in the context of real-time GPU research, where new algorithmic or analysis techniques should typically remain agnostic to the underlying GPU architecture. In support of this claim, we summarize how closed-source platforms have obstructed prior research using NVIDIA GPUs, and then demonstrate that AMD may be a viable alternative by modifying components of the ROCm software stack to implement spatial partitioning. Finally, we present a case study using the PyTorch deep-learning framework that demonstrates the impact such modifications can have on complex real-world software.

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Nathan Otterness and James H. Anderson. AMD GPUs as an Alternative to NVIDIA for Supporting Real-Time Workloads. In 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 165, pp. 10:1-10:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{otterness_et_al:LIPIcs.ECRTS.2020.10,
  author =	{Otterness, Nathan and Anderson, James H.},
  title =	{{AMD GPUs as an Alternative to NVIDIA for Supporting Real-Time Workloads}},
  booktitle =	{32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)},
  pages =	{10:1--10:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-152-8},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{165},
  editor =	{V\"{o}lp, Marcus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2020.10},
  URN =		{urn:nbn:de:0030-drops-123732},
  doi =		{10.4230/LIPIcs.ECRTS.2020.10},
  annote =	{Keywords: real-time systems, graphics processing units, parallel computing}
}

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.

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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)

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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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Documents authored by Otterness, Nathan

AMD GPUs as an Alternative to NVIDIA for Supporting Real-Time Workloads

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

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