Introduction to OpenVX

System Software Lab., NCTU
Introduction to OpenVX
An open, royalty-free standard
for cross platform acceleration of computer vision applications.
Presenter: Jia-Rung Chang
Instructor: Yi-Ping You
The last modification date : 2016/08/10

Outline
OpenVX’s role in the Khronos ecosystem
OpenVX in reality
Experience from Synopsys
OpenVX relevant paper
Conclusion

What is OpenVX?
An open standard , not open source.
Do not specify the details of implementation.
For cross platform acceleration of computer
vision applications.
Every vendor can do their optimization for their
own Soc.
Ex: NVIDIA, AMD and Synopsys,etc.
Currently,only few of vendors release their
own implementation.

Vision Acceleration
Targeted at low-power, real-time applications
Mobile and embedded platforms
Portability across diverse heterogeneous
processors
ISPs, Dedicated hardware, DSPs and DSP arrays, GPUs,
Multi-core CPUs …
Doesn’t require high-power CPU/GPU Complex
Low-power host can setup and manage frame-rate
vision processing pipeline

Khronos APIs for Vision Processing
ref: http://www.slideshare.net/embeddedvision/1-c01-khronostrevett

Layered Vision Processing Ecosystem
ref: http://www.slideshare.net/embeddedvision/1-c01-khronostrevett

OpenVX and OpenCV are Complementary
OpenCV OpenVX
Implementation
Community driven open source
library
Open standard API designed to be
implemented by hardware vendors
Conformance
Extensive OpenCV Test Suite but
no formal Adopters program
Implementations must pass defined
conformance test suite to use
trademark
Consistency
Available functions can vary
depending on implementation /
platform
All core functions must be available
in all conformant implementations

OpenVX Framework Objects Definition
Context
The object domain for all OpenVX objects.
Kernel
The abstract representation of a computer vision
function
Node
An instance of a kernel that will be paired with a
specific set of the parameters.
Graph
A set of nodes connected in a directed (only goes
one-way) acyclic (does not loop back) fashion.

OpenVX Framework
ref: https://www.khronos.org/openvx/

OpenVX Status
OpenVX 1.1 Specification released in 2016 at
Embedded Vision Summit
OpenVX 1.0 in 2014
Expands node functionality AND enhances graph
framework
The sample implementation v1.1 from Khronos is
failed to build.
The sample implementation v1.0.1 from Khronos can
build,but can not fully support their own tutorial.
Official tutorial uses AMDOVX(v1.0.1) as the default
OpenVX libraries.

NVIDIA VisionWorks
The implementation for OpenVX in
VisionWorks is not open source.
Available on Ubuntu 14.04(64-bits)
Because it is not an open source project, we skip
trying it.

AMD OpenVX (AMDOVX)
The official OpenVX tutorial use AMDOVX as
the default libraries.
In the tutorial
AMDOVX has better performance than the
“Khronos sample implementation”.
The “Khronos sample implementation” version 1.1 is
failed to build on my Ubuntu16.04 platform.
With AMD GPU will get better performance,
but not necessarily.
AMD official Linux driver stop updating since 2016.

Outline
OpenVX in reality
Focusing on the idea, not the details.
ref:Embeded VISION ALLIANCE
Conclusion

Outline
OpenVX in reality
Does not focus on the details,but the possible
direction of research for us.
Conclusion

Graph execution model in OpenVX
Synchronous blocking mode
In that vxProcessGraph() will block until the graph
has completed
Causing the synchronous processing of a graph.
Asynchronous single-issue-per-reference mode
via vxScheduleGraph() and vxWaitGraph().
Schedules a graph for future execution
Waits for a specific graph to complete.

Scheduling Methods for OpenVX Programs
on Heterogeneous Multi-core Systems
Authors: Tzu-Hsiang Lin, Cheng-Yen Lin, and Jenq-Kuen
Lee
Department of Computer Science, National Tsing-Hua University,
Taiwan
Source: PDPTA'15
The 21st International Conference on Parallel and Distributed
Processing Techniques and Applications

Keynotes
Assumption
A linear relationship for data size and
computation time, data size and data transfer
time
The graph execution model
A directed acyclic graph (DAG) that determines
the computation process of an OpenVX application
Optimization opportunities
Dispatching graphs to a different target, such as
multicore CPUs with C, OpenMP runtime, OpenCL
on GPUs, or a dedicated hardware.
Memory locality and system throughput.

Scheduling Algorithm(Node coarsen algorithm)
Two phases
Node coarsen algorithm
– Calculating the nodes that whether belonging to specific cluster via
thier ranking algorithm,including computation time and data transfer
time between host and targets.
– vxVerifyGraph(graph)
» Nodes in the same cluster will be forced to dispatch to the same target in the
scheduling phase
» We only select the nodes which can reduce data transfer time or largely shorten the
computation time into the same cluster
Node scheduling algorithm
– Dispatching the clustered nodes.
» Otherwise, finding the most free/available target compared to the preferred targets
for the nodes, and rank both of them.
» According to the rank,dispatch it.
– vxProcessGraph(graph)
» The remaining nodes not clustered will be scheduled for the target in the
scheduling(this) phase.
Keynotes

Keynotes
Experiment results

Supporting Real-Time Computer Vision
Workloads using OpenVX on
Multicore+GPU Platforms
Authors: Glenn A. Elliott, Kecheng Yang, and James H.
Anderson
Department of Computer Science, University of North Carolina at
Chapel Hill
Source: 2015 IEEE Real-Time Systems Symposium

Keynotes
The OpenVX model imposes three significant
implications on real-time scheduling
Having no notion of a repeating (i.e., periodic or
sporadic) task, and lacks any framework for real-
time analysis.
The key issue is the allowance of “back edges” that
can create cycles in a graph.
Does not defining a threading model for graph
execution.
Requiring a graph to execute end-to-end before it
may be executed again.
Significantly hindering the ability to exploit parallelism
by “pipelining” portions of a graph’s structure

Keynotes
Implementation model

Conclusion
The Possible direction of research in OpenVX
The world-first OpenVX benchmark
Scheduling Algorithm
OpenVX in embedded/real-time system

Main References
Khronos website
May 2014 Embedded Vision Summit Technical
Presentation: Khronos
May 2016 Embedded Vision Summit Technical
Presentation: Synopsys
OpenVX tutorial(official)
AMDOVX
khronos OpenVX sample
OpenVX specification

Introduction to OpenVX

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