CUPiDRT: Detecting Improper GPU Usage in Real-Time Applications

Computer-vision applications typically rely on graphics processing units (GPUs) to accelerate computations. However, prior work has shown that care must be taken when using GPUs in real-time systems subject to strict timing constraints; without such care, GPU use can easily lead to unexpected delays...

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Bibliographic Details
Published inProceedings / International Symposium on Object-Oriented Real-Time Distributed Computing pp. 86 - 95
Main Authors Amert, Tanya, Anderson, James H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2021
Subjects
Computer Vision
Graphics processing units
Graphics Processors
Hardware/Software Interfaces
Image Processing
Real-time systems
Runtime
Software libraries
Static analysis
Throughput
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Summary:Computer-vision applications typically rely on graphics processing units (GPUs) to accelerate computations. However, prior work has shown that care must be taken when using GPUs in real-time systems subject to strict timing constraints; without such care, GPU use can easily lead to unexpected delays not only on the GPU device but also on the host CPU. In this paper, a software library is presented that can detect the improper use of GPUs for safety-critical computer-vision applications. This library was used to analyze several GPU-using sample applications available as part of OpenCV, a popular computer-vision library, revealing the presence of issues in all ten applications considered. Additionally, a case study is presented, detailing the response-time improvements to one of the applications when such issues are corrected.
ISSN:2375-5261
DOI:10.1109/ISORC52013.2021.00022