Heterogeneous compute in computer vision: OpenCL in OpenCV

Harris Gasparakis

doi:10.1117/12.2054961

17 February 2014 Heterogeneous compute in computer vision: OpenCL in OpenCV

Harris Gasparakis

Proceedings Volume 9029, Visual Information Processing and Communication V; 90290L (2014) https://doi.org/10.1117/12.2054961
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

We explore the relevance of Heterogeneous System Architecture (HSA) in Computer Vision, both as a long term vision, and as a near term emerging reality via the recently ratified OpenCL 2.0 Khronos standard. After a brief review of OpenCL 1.2 and 2.0, including HSA features such as Shared Virtual Memory (SVM) and platform atomics, we identify what genres of Computer Vision workloads stand to benefit by leveraging those features, and we suggest a new mental framework that replaces GPU compute with hybrid HSA APU compute. As a case in point, we discuss, in some detail, popular object recognition algorithms (part-based models), emphasizing the interplay and concurrent collaboration between the GPU and CPU. We conclude by describing how OpenCL has been incorporated in OpenCV, a popular open source computer vision library, emphasizing recent work on the Transparent API, to appear in OpenCV 3.0, which unifies the native CPU and OpenCL execution paths under a single API, allowing the same code to execute either on CPU or on a OpenCL enabled device, without even recompiling.

Citation Download Citation

Harris Gasparakis "Heterogeneous compute in computer vision: OpenCL in OpenCV", Proc. SPIE 9029, Visual Information Processing and Communication V, 90290L (17 February 2014); https://doi.org/10.1117/12.2054961

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