Computer Vision Powered by Heterogeneous System Architecture (HSA) by Dr. Harris Gasparakis, AMD

Copyright © 2014 AMD 1
Dr. Harris Gasparakis
5/29/2014
Computer Vision Powered by
Heterogeneous System Architecture
(HSA)

• DEVELOPING EMBEDDED VISION APPLICATIONS: THE PROPRIETARY API LEGACY.
• THE RISE OF GPUS: DENSE DATA PARALLELISM AND CACHE-COHERENT SIMD
• DO WE STILL NEED CPUs?
• THE HETEROGENEOUS FUTURE OF VISION: OPENCL™, HSA
• OPENCL EVOLUTION
• OPENCL 1.X
• OPENCL 2.X AND HSA
• THE OPENCL EXECUTION MODEL
• MONSTERS IN THE ORCHESTRA, CES 2014
• PUTTING OPENCL™ 2.0 TO WORK
• OPENCL™ IN OPENCV
• OPENCV 3.0: THE TRANSPARENT API
• HOW DOES IT WORK?
• CONCLUDING THOUGHTS
AGENDA

• Choose HW and SW platform
– A multitude of devices of different
capabilities and strengths!
– A multitude of algorithms of different
requirements! Data Parallel (Y/N/M?)
• Highly specialized programmers
• Non-portable programs, with high
platform risk
Is Image
Processing/
Vision your
core IP?
Use somebody’s SDK
or end product
No
Yes
This talk is
not for you
Developing Embedded Vision Applications

Sobel
Just SIMD is NOT
good enough for
good GPU
acceleration,
contrary to
popular wisdom
Merge kernels
Split kernels
• “GPU is great for SIMD: Single Instruction Multiple Data”
• Image Processing = Dense Data Parallelism
• Same calculation (e.g. calculate edge strength) for all pixels.
• Adjacent threads load adjacent “enough” data
1. Too simple algorithms (non enough math per memory transfer)
2. Too much complexity per kernel (high register pressure)
The rise of GPUs: Dense Data Parallelism

Features
• Extensive set of CPU libraries
• Several approaches to vision (image understanding) can be thought of as
a “dense to sparse transition”
• Sparsity is not a GPU’s friend.
• OpenCL 2.0 solves this problem much more optimally
(and with less code)
Do we still need CPUs?

CPU
GPU
Audio
Processor
ISP: Image
Signal
Processing
Fixed
Function
Acctr
Encode
Decode
SharedMemory
DSP
Other!
The Heterogeneous Future of Vision
THE RIGHT IP FOR THE RIGHT TASK!
• CPU is great for serial tasks
• Lower latency
• Good branching performance
• Lower throughput
• Good at Task parallelism
• Better for Sparse Data Parallelism
• GPU excels at data parallel problems
• High throughput
• Possibly High latency
• Good at “Dense Data Parallelism”
• Increasingly better at task parallelism
(concurrent kernel execution, and
OpenCL 2.0 Dynamic parallelism)
An efficient Heterogeneous System Architecture would be optimal (e.g. GFLOPS/$/W)

© Copyright 201 HSA Foundation. All Rights Reserved.7
Founders
Promoters
Supporters
Contributors
Academic
HSA FOUNDATION

• OpenCLTM: Khronos Software API
• Cross-platform (Windows, Linux, Mac OS, etc.)
• Multi-vendor (AMD, Apple, IBM, Intel, NVIDIA, etc.),
with maturing support
• Multicore CPU, discrete GPU, integrated GPU (aka
APU), DSP, FPGA, etc.
• HSA: Heterogeneous System Architecture, an industry
standard specification
• OpenCL 2.0 introduces HSA features
• Open Source also helps!
• OpenCV, featuring OpenCL acceleration
Open Standards

OpenCL™ Evolution: Discrete GPU
OpenCL was invented as
an open standards high
level API for GPU
compute, first on discrete
graphics cards
OpenCL abstracts:
• Data management across
multiple memory spaces
• Memory buffers / Images
• Compute Instructions
• “Kernels”
• Execution on “compute
units” CU.
PCIe
™
CU CU CU
CU CU CU CU
CU CU CU CU
GPU device
Memory
GPU
Main memory
Host
Memory
PCIe
Memory
(pinned)
CPU
…

OpenCL™ Evolution: Legacy APU
CU CU
…
CU CU …CU CU CU CU
GPU
 APU: Physical
Integration: CPU
and GPU on
same die
 OpenCL (1.x)
works also on
APUs
• Device memory
is (part of) main
memory, but
still must use
memory
buffers!
Host memory Device Visible
Host Memory
Device memory
Host Visible
Device Memory
Main memory
CPU
…

OpenCL™ Evolution: HSA Enabled APU
 Unified Coherent
Memory enables
data sharing
across all
processors and
GPU compute
units
 OpenCL™ 2.x: No
need to use
memory buffers,
just use data
pointers, just
like you would do
on the CPU.
Unified (Bidirectionally Coherent, pageable) Virtual Memory
CU CU
…
CU CU
…
CU CU CU CU
GPUCPU
Cache Cache
Physical Memory

A 360o x 90o immersive gesture-enabled experience, enabled by OpenCL (and OpenCV)
Monsters in the Orchestra, AMD CES 2014

VISION: Dense to sparse transition
Putting OPENCL™ 2.0 to Work
GPU
keypoints
Data changed by a kernel, can be visible by CPU, before
kernel returns requires (fine grain SVM).
CPU
consumes
keypoints
“as they
come”, and
updates a
“shape
model”

Device
Setup
Compile
Kernels
Allocate
Memory
Further
Processing
Clean up
Other
Tasks…
Host
Memory Transfer
(discrete GPU)
Or Zero Copy (APU
OpenCL 1.2)
Or Shared Virtual Memory
(APU OpenCL 2.0)
Kernel
1
Kernel
2
Kernel
2_1
Kernel
2_2
Kernel
Compute Device
New in OpenCL
2.0: Dynamic
parallelism! A
kernel can
enqueue
another kernel
The OpenCL Execution Model

Open Computer Vision Library
2,500+ algorithms and functions Cross-platform BSD license
High performance Professionally developed 7M+ downloads
• OpenCL is fully integrated in OpenCV
• ~100 most commonly used algorithms optimized with OpenCL
• Can be built without OpenCL SDK installed. Dynamic OpenCL runtime loading
• OpenCL enabled on the official Windows bin pack
• OpenCV pre-commit check includes OpenCL tests
• Very easy to plug in your own kernels using OpenCV plumbing
• In 2.4.x, OpenCL acceleration is a distinct code path
First public
release
2000 2013 ~10/2014
v2.0
C++ API
v2.4.3
OpenCL™
2009
v3.0 alpha
Transparent API

// initialization
VideoCapture vcap(...);
CascadeClassifier
fd("haar_ff.xml");
Mat frame, frameGray;
vector<Rect> faces;
for(;;){
vcap >> frame;
cvtColor(frame, frameGray,
BGR2GRAY);
equalizeHist(frameGray,
frameGray);
fd.detectMultiScale(frameGr
ay, faces);
}
OCV 2.4: Face detect on CPU
// initialization
ocl::OclCascadeClassifier
fd("haar_ff.xml");
ocl::oclMat frame, frameGray;
Mat frameCpu;
vector<Rect> faces;
for(;;){
vcap >> frameCpu;
frame = frameCpu;
ocl:: cvtColor(frame, frameGray,
BGR2GRAY);
ocl:: equalizeHist(frameGray,
frameGray);
ocl::
fd.detectMultiScale(frameGray,
faces);
}
OCV 2.4: Face detect using OpenCL™
OpenCV 2.4: Similar, but not identical code paths. You will
need to write code explicitly for both CPU and OpenCL
// initialization
CascadeClassifier
fd("haar_ff.xml");
UMat frame, frameGray;
vector<Rect> faces;
for(;;){
vcap >> frame;
cvtColor(frame, frameGray,
BGR2GRAY);
equalizeHist(frameGray,
frameGray);
fd.detectMultiScale(frameGray
, faces);
}
OCV 3.0: Face detect Anywhere!
This code will run, and configure itself
differently on different platforms!
The Need for a Transparent API

UMat:
UMatData:
Reference counts
Dirty bits
Opaque handles (e.g. clBuffer)
CPU data
GPU data
Handles data synchronization efficiently
Mat:
getMat(…)
getUMat(…)
• Easy transition path from 2.x to 3.x. Code that used to work in 2.x,
should still work. Therefore, cv::Mat is still around.
Both Mat and UMat are views into UMatData, which does the heavy lifting
How does OpenCV 3.0 T-API work?

• OpenCL™ provides a non-proprietary API suitable for image processing
and vision applications, that works well on multiple platforms
• OpenCL 2.0 and HSA enable efficient collaboration between CPU and
GPU cores, on equal footing. An evolution that can only be compared to
the one from single core to multi-core CPUs!
• OpenCV contains lots of OpenCL examples that can be a great starting
point for your own projects.
Join the Open Standards evolution!
Concluding Thoughts

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ATTRIBUTION
© 2014 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo and combinations thereof are trademarks
of Advanced Micro Devices, Inc. in the United States and/or other jurisdictions. OpenCL is a trademark of Apple Inc. used by
permission by Khronos. Other names are for informational purposes only and may be trademarks of their respective owners.
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Computer Vision Powered by Heterogeneous System Architecture (HSA) by Dr. Harris Gasparakis, AMD

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