BIOMEDICAL DATA ANALYSIS ON HETEROGENEOUS PLATFORM. Dong Ping Zhang Heterogeneous System Architecture AMD

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2 BIOMEDICAL DATA ANALYSIS ON HETEROGENEOUS PLATFORM Dong Ping Zhang Heterogeneous System Architecture AMD

3 VASCULATURE ENHANCEMENT 3 Biomedical data analysis on heterogeneous platform June, 2012

4 EXAMPLE: COMPUTED TOMOGRAPHY IMAGE Before enhancement After enhancement with bone structure 4 Biomedical data analysis on heterogeneous platform June, 2012

5 EXAMPLE: MAGNETIC RESONANCE ANGIOGRAM Before enhancement After enhancement 5 Biomedical data analysis on heterogeneous platform June, 2012

6 VASCULATURE ENHANCEMENT Before enhancement? After enhancement 6 Biomedical data analysis on heterogeneous platform June, 2012

7 VASCULATURE ENHANCEMENT ALGORITHM Gaussian convolution (three 1D kernels instead of one 3D kernel) Hessian matrix computation and analysis Eigen decomposition Eigenvector + eigenvalue sorting 7 Biomedical data analysis on heterogeneous platform June, 2012

8 ALGORITHM: FIRST THREE COMPONENTS Analyse the main modes of 2 nd -order variation in image intensity to determine the type of local structure Shape space for Hessian matrix in 3D [1] [1]: Q. Lin. PhD thesis, 2003, Enhancement, Extraction, and Visualization of 3D Volume Dataset 8 Biomedical data analysis on heterogeneous platform June, 2012

9 9 Biomedical data analysis on heterogeneous platform June, 2012 z z L y z L x z L z y L y y L x y L y x L y x L x x L H e 3 e 1 e 2 Compute Hessian matrix of ) ( ) ( ) ( z G y G x G I L Vessel segment representation at voxel x ALGORITHM: FIRST THREE COMPONENTS x Table: List of geometric patterns in 2D and 3D, depending on the sign and magnitude of the eigenvalues. H: high value; L: low value; +/-: the sign of the eigenvalue.

10 ALGORITHM Gaussian convolution (three 1D kernels instead of one 3D kernel) Hessian matrix computation and analysis Eigen decomposition Eigenvector + eigenvalue sorting Vesselness computation (calculating how likely a voxel being part of vascular network) 10 Biomedical data analysis on heterogeneous platform June, 2012

ALGORITHM: VESSELNESS Compute single scale vesselness

S 2 2 if 0 or 2 3 otherwise 0 where R A 2 3 R B 1 2 3 S i

scales: V max min max Tensor voting to bridge the gaps V

11 ALGORITHM: VESSELNESS Compute single scale vesselness response from a scale : V 1 e 2 RA 2 2 e 0 2 RB e 2 S 2 2 if 0 or 2 3 otherwise 0 where R A 2 3 R B S i 2 i Compute maximal vesselness response from multiple scales: V max min max Tensor voting to bridge the gaps V 11 Biomedical data analysis on heterogeneous platform June, 2012

12 PERFORMANCE CPU + CAYMAN GPU Experiment setting: Input CTA image: 256 x 256 x 200 voxel, voxel size 0.62 x 0.62 x 0.5 mm Application parameters: 7 scales distributed in range 0.5-4mm Hardware: AMD Phenom TM II X6 1090T, Radeon TM HD 6970 Comparison: Execution time (s) TBB(conv), hmatrix, eigen, v OpenCL(conv+hmatrix+v), eigen OpenCL(conv+hmatrix+v),TBB(eigen) 83 OpenCL(conv+h+eigen+v) Biomedical data analysis on heterogeneous platform June, 2012

PERFORMANCE CPU + CAYMAN GPU APP Profiling Windows of scenario 4: Projection

1% Data transfer (read & write) + launch latency (kernel launch, read launch

8s by eliminating data transfer, launch latency and unaccounted activities.

13 PERFORMANCE CPU + CAYMAN GPU APP Profiling Windows of scenario 4: Projection (HSA Simulator): Kernel execution: 35.1% Data transfer (read & write) + launch latency (kernel launch, read launch and write launch): 41.7% Execution time is reduced to 3.8s by eliminating data transfer, launch latency and unaccounted activities. Kernel execution 35.1% Data transfer 32.7% Launch latency 9% unaccounted activities 13 Biomedical data analysis on heterogeneous platform June, 2012

14 PERFORMANCE LLANO Per-kernel execution time (% of sum) and ISA statistics 14 Biomedical data analysis on heterogeneous platform June, 2012

15 PERFORMANCE LLANO Per-size/direction data transfer time (% of sum) Breakdown of command durations and projected APU performance 15 Biomedical data analysis on heterogeneous platform June, 2012

16 PERFORMANCE CPU + TAHITI GPU Experiment: CTA image: 256 x 256 x 200 voxel, voxel size 0.62 x 0.62 x 0.5 mm Application parameters: 7 scales distributed in range 0.5-4mm Hardware: AMD Phenom TM II X6 1090T + Radeon TM HD 7950 Execution time (s) TBB(conv), hmatrix, eigen, v OpenCL(conv+hmatrix+v), eigen OpenCL(conv+hmatrix+v),TBB(eigen) 81.8 OpenCL(conv+h+eigen+v) Biomedical data analysis on heterogeneous platform June, 2012

5-4mm Hardware: AMD Phenom TM II X6 1090T + Radeon TM HD 6970 Architecture Overview: AMD

17 PERFORMANCE COMPARISON CAYMAN AND TAHITI Experiment: CTA image: 256 x 256 x 200 voxel, voxel size 0.62 x 0.62 x 0.5 mm Application parameters: 7 scales distributed in range 0.5-4mm Hardware: AMD Phenom TM II X6 1090T + Radeon TM HD 6970 Architecture Overview: AMD Phenom TM II X6 1090T + Radeon TM HD Biomedical data analysis on heterogeneous platform June, 2012

18 PERFORMANCE COMPARISON CAYMAN AND TAHITI Experiment: CTA image: 256 x 256 x 200 voxel, voxel size 0.62 x 0.62 x 0.5 mm Application parameters: 7 scales distributed in range 0.5-4mm Hardware: AMD Phenom TM II X6 1090T + Radeon TM HD 6970 AMD Phenom TM II X6 1090T + Radeon TM HD 7950 Execution time comparison TBB(conv), hmatrix, eigen, v OpenCL(conv+hmatrix+v), eigen OpenCL(conv+hmatrix+v),TBB(eigen) OpenCL(conv+h+eigen+v) % seconds Phenom TM II X6 + Tahiti Phenom TM II X6 + Cayman 18 Biomedical data analysis on heterogeneous platform June, 2012

19 PERFORMANCE TRINITY Per-kernel execution time (% of sum) and ISA statistics 19 Biomedical data analysis on heterogeneous platform June, 2012

20 PERFORMANCE TRINITY Per-size/direction data transfer time (% of sum) Breakdown of command durations and projected APU performance 20 Biomedical data analysis on heterogeneous platform June, 2012

21 VASCULATURE SEGMENTATION 21 Biomedical data analysis on heterogeneous platform June, 2012

22 VASCULATURE EXTRACTION RIDGE TRAVERSAL Ridge points satisfy: Ridgeness function: 22 Biomedical data analysis on heterogeneous platform June, 2012

23 VASCULATURE EXTRACTION RIDGE TRAVERSAL 23 Biomedical data analysis on heterogeneous platform June, 2012

24 VASCULATURE EXTRACTION GRAPH SEARCH Using A* graph search to find minimal cost path from start to end nodes: 24 Biomedical data analysis on heterogeneous platform June, 2012

25 VASCULATURE EXTRACTION GRAPH SEARCH RESULTS 25 Biomedical data analysis on heterogeneous platform June, 2012

26 VASCULATURE EXTRACTION TEMPLATE-BASED APPROACH Tubular template: 26 Biomedical data analysis on heterogeneous platform June, 2012

27 VASCULATURE EXTRACTION TEMPLATE-BASED APPROACH Modeling local image region: Template fitting: 27 Biomedical data analysis on heterogeneous platform June, 2012

28 PERFORMANCE TEMPLATE-BASED APPROACH Experiment setting: Input CTA image: 256 x 256 x 200 voxel, voxel size 0.62 x 0.62 x 0.5 mm Template size: 17 x 17 x 17 voxels, voxel size 0.62 x 0.62 x 0.5 mm Hardware: AMD Phenom TM II X6 1090T, Radeon TM HD 7970 regions D Vessel Template Match seconds multi-threaded single-threaded 28 Biomedical data analysis on heterogeneous platform June, 2012

29 VESSEL SEGMENTATION FRAMEWORK Input is a vasculature image I, output are the vascular trees in that image. Step 1: Parallel_for_each voxel x in the input image I { compute the vesselness value v(x) representing how likely a voxel is part of the vessel structure. } Step2: Compute cost image from input image I, using vesselness image V from previous step Step3: Select starting and ending nodes, do A* graph search to compute the minimal cost path; Bifurcation are dealt with here semi-automatically. Output: vascular tree. Step 4: Parallel_for_each (voxels in the vascular tree) { do template-fitting at local region of input image. } 29 Biomedical data analysis on heterogeneous platform June, 2012

30 VASCULAR SEGMENTATION FRAMEWORK BIFURCATION 30 Biomedical data analysis on heterogeneous platform June, 2012

31 VESSEL SEGMENTATION FRAMEWORK Input is a vasculature image I, output are the vascular trees in that image. Step 1: Parallel_for_each voxel x in the image I { compute the vesselness value v(x) representing how likely a voxel is part of the vessel structure. } Step2: Sort all voxels based on v(x) value; Nested enqueue? Step 3: Parallel_for_each (voxels with a high probability of being part of the vascular system) { A: Extract the vascular branch from that voxel (noted as parent voxel) by template-match algorithm; If (Bifurcation is detected) { Parallel_for_each(children voxels) { B: same as A; } } } 31 Biomedical data analysis on heterogeneous platform June, 2012

32 VESSEL SEGMENTATION FRAMEWORK Big data-parallel single-layer dispatch is not necessarily the appropriate abstraction for representing the programmer s problem. Key component in vessel lumen segmentation is template match: foreach block: while(found closest match): foreach element in comparison Serial code mixed in with two levels of parallel code So what if instead of trying to do a single flat dispatch we explicitly layer the task launches? 32 Biomedical data analysis on heterogeneous platform June, 2012

VESSEL SEGMENTATION FRAMEWORK LAYERED DISPATCH parallelfor(int numthreads [](int index){ // scalar functor // do scalar stuff

$Initialize(); sumsq = FLOAT_MAX; while( sumsq still being minimized ){ X = estimating new position from X Accumulator<float>$ diff*diff; }); // end localparallelfor If sumsq > acc // See if it s small enough or continue search using heuristic X = X}});

diff*diff; }); // end localparallelfor If sumsq > acc // See if it s small enough or continue search using heuristic X = X}});

A One serial launched loop that for searches each block for the in best the match. data set.

33 VESSEL SEGMENTATION FRAMEWORK LAYERED DISPATCH parallelfor(int numthreads [](int index){ // scalar functor // do scalar stuff // for example, to do a template match we might do X.Initialize(); sumsq = FLOAT_MAX; while( sumsq still being minimized ){ X = estimating new position from X Accumulator<float> acc; localparallelfor(template size, [=X](int3 index) { auto diff = templatedata(x + index) - imagedata(x + index); acc += diff*diff; }); // end localparallelfor If sumsq > acc // See if it s small enough or continue search using heuristic X = X}}); A parallel thread launch: One wavefront/workgroup launched for each index. A One serial launched loop that for searches each block for the in best the match. data set. This is best written as a serial loop and is clean scalar Parallel code. Extracting loop: this from the Covers parallel each context pixel can in the feel block messy. being compared. Easily vectorisable and with no launch overhead. 33 Biomedical data analysis on heterogeneous platform June, 2012

34 VESSEL EXTRACTION FRAMEWORK FUTURE Bolt Bolt::Parallel_for_each (voxel x in the image I) { compute how likely a voxel is part of the vessel structure: v(x); construct vesselness image V } Bolt::Sort (voxels of image V ) Create initial list L of N voxels with highest probability of being part of vessel structure. Bolt::parallel_do (L) { Implement template match process with bolt::parallel_for_each; If bifurcation is detected, enqueue new voxel to L }; BOLT: A C++ Template Library for HSA, Ben Sander, 5:15 PM, Wednesday 13 th June 34 Biomedical data analysis on heterogeneous platform June, 2012

35 VESSEL EXTRACTION FRAMEWORK FUTURE Channel Can GPGPU programming be liberated from the data-parallel bottleneck? 1:15 PM, Tuesday 12 th June HSA memory and execution model, HSA runtime 3:15 PM, Wednesday 13 th June Lee Howes 35 Biomedical data analysis on heterogeneous platform June, 2012

36 ACKNOWLEDGEMENT Lee Howes, Jay Cornwall Heterogeneous System Architecture, AMD Daniel Rueckert Imperial College London Trademark Attribution AMD, the AMD Arrow logo, Phenom TM, Radeon TM and combinations thereof are trademarks of Advanced Micro Devices, Inc. in the United States and/or other jurisdictions. Other names used in this presentation are for identification purposes only and may be trademarks of their respective owners Advanced Micro Devices, Inc. All rights reserved. 36 Biomedical data analysis on heterogeneous platform June, 2012

37 Disclaimer & Attribution The information presented in this document is for informational purposes only and may contain technical inaccuracies, omissions and typographical errors. The information contained herein is subject to change and may be rendered inaccurate for many reasons, including but not limited to product and roadmap changes, component and motherboard version changes, new model and/or product releases, product differences between differing manufacturers, software changes, BIOS flashes, firmware upgrades, or the like. There is no obligation to update or otherwise correct or revise this information. However, we reserve the right to revise this information and to make changes from time to time to the content hereof without obligation to notify any person of such revisions or changes. NO REPRESENTATIONS OR WARRANTIES ARE MADE WITH RESPECT TO THE CONTENTS HEREOF AND NO RESPONSIBILITY IS ASSUMED FOR ANY INACCURACIES, ERRORS OR OMISSIONS THAT MAY APPEAR IN THIS INFORMATION. ALL IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE ARE EXPRESSLY DISCLAIMED. IN NO EVENT WILL ANY LIABILITY TO ANY PERSON BE INCURRED FOR ANY DIRECT, INDIRECT, SPECIAL OR OTHER CONSEQUENTIAL DAMAGES ARISING FROM THE USE OF ANY INFORMATION CONTAINED HEREIN, EVEN IF EXPRESSLY ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. AMD, the AMD arrow logo, and combinations thereof are trademarks of Advanced Micro Devices, Inc. All other names used in this presentation are for informational purposes only and may be trademarks of their respective owners Advanced Micro Devices, Inc. 37 Biomedical data analysis on heterogeneous platform June, 2012

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