hc-4020, enhancing opencl performance in aftershot pro with hsa, by michael wootton

ENHANCING OPENCL PERFORMANCE IN COREL AFTERSHOT™ PRO WITH HSA

2 | Enhancing OpenCL Performance in Corel AfterShot™ Pro with HSA | NOVEMBER 19, 2013

COREL AFTERSHOT™ PRO

What is Corel AfterShot™ Pro?

Corel AfterShot™ Pro is photo workflow software

Non-destructive photo editing of JPEG, TIFF, and Raw formats from hundreds of cameras

Photo Management

Batch Processing of modified files

AfterShot Pro

Basics


INSIDE AFTERSHOT

Architectural Features: ‒ Task Scheduling

‒ Tile Processing


AFTERSHOT TASK MANAGEMENT

Work is broken down into Tasks. Tasks typically: ‒ Contain execution logic (code)

‒ May store resultant data

‒ Track whether they are complete

The Task Scheduler: ‒ Allocates a worker thread per CPU core

‒ Runs Tasks based on priority

‒ Allows Tasks to block on each other

File Reader Photo

Thumbnail

JPEG Decoder

Disk

Data

Task Dependency

A Simple Task Dependency Graph


PROCESSING WITH TILES

The standard simpler approach is to use large monolithic images

Images are broken down into tiles for processing

Tiling provides faster screen updates. Only compute the visible parts of the image

Tiling allows more effective memory management


PROCESSING WITH TILES CONTINUED

The Image Processing Pipeline is made up of several discrete steps [or filters]

To process a single tile: ‒ Load the input data (e.g. raw or jpeg data)

‒ Apply each Filter step in turn

Generally, we only need the output of the last step, the top Tile in the Stack

Raw Data Final Image


ADVANCED TILE PROCESSING

Some Image Filters require a radius of pixels as input

Partially processed neighbor Tiles must complete before the main Tile can continue

Intermediate Tiles must be stored in memory so they do not rerun

Example Filters: ‒ Sharpening

‒ Lens Correction

‒ Noise Reduction

‒ Cropping Requires multiple source tiles

OpenCL™ in AfterShot Pro


ACCELERATING AFTERSHOT WITH OPENCL™

Goals for the AfterShot Pro OpenCL port

Offload image processing from Tiles

Work within the existing System ‒ Contain changes to a few critical modules

‒ Maintain full CPU utilization

‒ Integrate OpenCL Events into the Task System


GETTING WORK TO OPENCL

Identify the longest running image Filter functions and replace them with OpenCL kernels

Do not block CPU threads, use OpenCL event callbacks.

Processing becomes Asynchronous

Limit total work in flight to conserve memory

Marshall data automatically


CAVEATS OF ASYNCHRONOUS OPENCL PROCESSING

High Buffer Usage ‒ Each kernel that runs needs input, output, and possibly scratch buffers.

‒ Buffers must “stick around” until the kernels complete

‒ Multiple chains of kernels a needed to keep the GPU busy

Kernel 4

Kernel 5

Buffer

Kernel 3

Kernel 2

Kernel 1

Buffer Buffer Buffer Buffer Buffer

Buffer

Processing one 512 x 512 image requires multiple 3 MB buffers resident in device memory (VRAM)


CAVEATS OF ASYNCHRONOUS OPENCL PROCESSING – CONTINUED

Dependencies Must Be Resolved in Advance ‒ For best performance all kernels in a chain should be enqueued together

‒ The state of all dependencies must be known before the first kernel is queued

‒ Difficult to track

‒ Compromise: only use OpenCL for Filters with simple linear dependencies

Kernel chaining and asynchronous execution provides excellent GPU utilization.

OpenCL Challenges


LARGE RADIUS IMAGE FILTERS

Several image processing operations require neighbor pixels. In AfterShot image Filters are broken down into one of two categories:

Normal Only requires the local Tile

Large Radius Requires multiple Tiles


LARGE RADIUS IMAGE FILTERS ARE DIFFICULT

Large Radius AfterShot Filters are particularly difficult to implement in OpenCL

Large Radius filters will “break” kernel chaining

A extra layer of Intermediate Tiles must be resident, which will: ‒ Exhaust Device Memory, or

‒ Cause excessive bus transfers, hurting performance

And the solution is…


LARGE RADIUS FILTERS - NO

Don’t do it.

Large Radius filters are possible but at great development cost

Performance would ultimately depend on tricky optimizations

Large radius filters were left to run on the CPU


AFTERSHOT OPENCL RESULTS

Approximately 70% of image processing work was moved off of the CPU cores*

Batch processing speed improved by 3.5x*

Maintains 100% utilization on 8 CPU cores*

Only a mid-level GPU is required

Supported on Windows, Linux, and OS X

AfterShot Pro with OpenCL was a success

*measured on developer’s system

OpenCL 2.0 SVM


OPENCL 2.0 SHARED VIRTUAL MEMORY

OpenCL 2.0 introduces Shared Virtual Memory (SVM)

Basic [Coarse Grain] SVM ‒ Host and kernels can share pointers

Advanced [Fine Grain] SVM is available on some hardware ‒ Host and kernels can operate concurrently on the same memory

Fine Grain System SVM ‒ Kernels can access the entire host process’ address space. Kernels can read or write malloc

buffers

‒ System SVM can greatly simplify buffer management in an OpenCL application

AfterShot Redux


RECONSIDERING LARGE RADIUS FILTERS

Large Radius OpenCL filters were dropped as an AfterShot feature. The reasons were both technical and resource related

Can System SVM make Large Radius AfterShot filters feasible? Signs point to yes ‒ No Device Memory required for Intermediate buffers

‒ Input streams from SVM, no buffer transfers

‒ Behavior more in-line with Software [non-OpenCL] filters

‒ Dependencies could be resolved just as they would for a Software filter


LOCAL CONTRAST – A LARGE RADIUS AFTERSHOT FILTER

The next version of AfterShot Pro will contain a new Local Contrast filter.

‒ GPU accelerated on systems with OpenCL and SVM.

‒ Increases image contrast in detailed areas while leaving large constant areas unchanged

‒ The effect is achieved through a large radius Unsharp Mask (10-20% of the overall image width)


SETTING UP A KERNEL TO USE SVM MEMORY


LOADING SVM MEMORY FROM INSIDE THE KERNEL


LOCAL CONTRAST RESULTS

System SVM simplified Local Contrast ‒ No complicated buffer management

‒ No clever optimizations were required to hide Device memory transfers

‒ Additional memory pressure is similar to a software filter

Performance is good. The OpenCL code runs in ¼ the time of the optimized software filter*

*measured on developer’s system


THANK YOU

Questions


DISCLAIMER & ATTRIBUTION

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ATTRIBUTION

© 2013 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. SPEC is a registered trademark of the Standard Performance Evaluation Corporation (SPEC). OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos. Other names are for informational purposes only and may be trademarks of their respective owners.

hc-4020, enhancing opencl performance in aftershot pro with hsa, by michael wootton

Technology

corel aftershot pro

enhancing opencl performance

aftershot opencl results

aftershot image filters

opencl kernels

aftershot pro basics

opencl goals

opencl events