Heterogeneous Managed Runtime Systems: A Computer Vision Case Study

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • External authors:
  • James Clarkson
  • Andrey Rodchenko
  • Andy Nisbet
  • John Mawer
  • Mikel Luján

Abstract

Real-time 3D space understanding is becoming prevalent across a wide range of applications and hardware platforms. To meet the desired Quality of Service (QoS), computer vision applications tend to be heavily parallelized and exploit any available hardware accelerators. Current approaches to achieving real-time computer vision, evolve around programming languages typically associated with High Performance Computing along with binding extensions for OpenCL or CUDA execution.

Such implementations, although high performing, lack portability across the wide range of diverse hardware resources and accelerators. In this paper, we showcase how a complex computer vision application can be implemented within a managed runtime system. We discuss the complexities of achieving high-performing and portable execution across embedded and desktop configurations. Furthermore, we demonstrate that it is possible to achieve the QoS target of over 30 frames per second (FPS) by exploiting FPGA and GPGPU acceleration transparently through the managed runtime system.

Bibliographical metadata

Original languageEnglish
Title of host publicationProceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Pages74-82
Number of pages9
ISBN (Print)978-1-4503-4948-2
DOIs
StatePublished - 2017

Publication series

NameVEE '17
PublisherACM