Accelerating Proton Computed Tomography with GPUson-demand.gputechconf.com/gtc/2015/presentation/S5497-Thomas-Uram.pdf · Accelerating Proton Computed Tomography with GPUs...

Accelerating Proton Computed Tomography with GPUs

Thomas'D.'Uram,'Argonne'Leadership'Compu2ng'Facility'Michael'E.'Papka,'Argonne'Leadership'Compu2ng'Facility,'Northern'Illinois'University'Nicholas'T.'Karonis,'Northern'Illinois'University,'Argonne'Na2onal'Laboratory

Argonne'Leadership'Compu2ng'Facility'E'Thomas'D.'Uram'([email protected])

Overview‣ Proton'computed'tomography'(pCT)'is'an'alterna2ve'to'xEray'based'CAT'scans,'which'

promises'several'medical'benefits'at'the'cost'of'being'significantly'more'computa2onally'expensive'

‣ We'designed'a'60Enode'GPU'cluster'to'meet'the'computa2onal'challenge'!

!

‣ Computed'tomography'‣ Benefits'of'proton'computed'tomography'‣ Computa2onal'problem'descrip2on'‣ CPU/GPU'performance'comparison

2

mailto:[email protected]


What is Computed Tomography?‣ CAT'(or'CT)'scans'are'wellEknown'‣ CAT'=='“computerized'axial'tomography”'‣ CAT'scans'are'used'to'reconstruct'the'density'distribu2on'within'a'volume,'typically'used'

in'medical'imaging'‣ CAT'scans'are'conducted'with'photons'(XErays)'

!

‣ What'is'Proton'Computed'Tomography?'• A'reconstruc2on'technique'similar'to'XEray'computed'tomography,'conducted'with'

protons'instead'of'photons

3


‣ 13'million'people'are'diagnosed'with'cancer'each'year'worldwide'‣ 2.6'million'of'them'are'candidates'for'proton'therapy'treatment'‣ Proton'therapy'involves'deposi2ng'protons'at'precise'loca2ons'within'a'tumor'

site'where'they'irradiate'the'target'2ssue'‣ The'protons'emit'lower'radia2on'as'they'travel'through'the'body'un2l'they'

reach'the'target,'where'they'emit'a'burst'of'radia2on'(the'Bragg'peak)'• Healthy'2ssue'beyond'the'tumor'site'receives'nominally'no'radia2on'

‣ It'is'crucially'important'to'precisely'iden2fy'the'tumor'site'• To'ensure'that'cancerous'2ssue'is'destroyed'• To'avoid'damaging'healthy'2ssue'surrounding'the'tumor,'especially'in'

sensi2ve'areas'‣ Proton'therapy'treatment'planning'is'currently'performed'using'XEray'imaging'

• Photons'and'protons'interact'with'intermediate'material'differently'• Conversion'between'photon/proton'modali2es'involves'a'systema0c'range'

error'of'365%


Why Proton Computed Tomography?

4

Image source: Wikipedia



‣ Our'goal'is'to'reconstruct'volume'of'adult'human'head'in'under'10'minutes''

‣ Protons'directed'through'two'frontal'planes,'the'target'volume,'two'backing'planes,'and'finally'a'calorimeter'

‣ Measures'posi2on'and'angle'of'incidence'of'protons'at'entry'and'exit,'and'the'energy'loss

5

Final System (in black): 4 tracking planes with XY Si detectors: calorimeter with 64 end=on CsI Crystals

Planned Scaled Prototype (in red): 4 planes of XY Si detectors (2 X-SSDs and 2 Y-SSDs per plane): 8 CsI Crystal bars

Calorimeter: Each bar corresponds to a 5cm x 5cm CsI Crystal, read out by a photodiode

Tracking Plane: Each large square corresponds to one double-sided or two single-sided 9cm x 9cm SSDs

Proton computed tomography



Problem Description‣ Proton'source,'detector'planes,'and'calorimeter'

mounted'on'rota2ng'gantry,'as'in'familiar'XEray'CT'configura2ons'

‣ Data'collected'over'a'full'rota2on'of'the'gantry,'180'samples'(every'2'degrees)'

‣ Ini2al'detector'designed'to'image'a'human'head'(nominally'25cm'cube)'

‣ From'physics'domain,'and'so'that'each'voxel'is'sufficiently'represented'in'the'resul2ng'system'matrix,'we'approximate'requiring'a'volume'consis2ng'of'256x256x36'(2,359,296=~'2.4M)'voxels'and'2'billion'protons'total'

‣ For'each'proton,'we'track'11'values:'‣ [x,y,z]'at'entry'‣ [x,y,z]'at'exit'‣ angle'at'entry'and'exit'‣ input'and'output'energy'‣ gantry'rota2on'angle

6

Final System (in black): 4 tracking planes with XY Si detectors: calorimeter with 64 end=on CsI Crystals

Planned Scaled Prototype (in red): 4 planes of XY Si detectors (2 X-SSDs and 2 Y-SSDs per plane): 8 CsI Crystal bars

Calorimeter: Each bar corresponds to a 5cm x 5cm CsI Crystal, read out by a photodiode

Tracking Plane: Each large square corresponds to one double-sided or two single-sided 9cm x 9cm SSDs



Baseline execution times

7

‣ Began'with'serial'code'that'took'more'than'7'hours'to'process'131M'protons'

‣ Parallelized'with'MPI'to'use'mul2ple'CPUs'

‣ Established'baseline'execu2on'2mes

{Phase Execution time (seconds)

Setup 128.2

Most Likely Path (MLP) 1278.5

Linear solver (CARP) 664.9

Overall execution time 2072.0

1 billion protons, 60 nodes, CPU only



MLP (Most Likely Path)

8

‣ In'contrast'with'XEray'computed'tomography'in'which'the'par2cles'traverse'the'volume'in'straight'lines,'in'pCT'the'protons'are'scakered'by'the'material'as'they'travel'through'the'volume'

‣ MLP'computes'the'path'integral'of'the'protons'through'the'material'based'on'their'known'entry'and'exit'loca2ons'and'angles'and'the'energy'loss'

‣ The'proton'paths'are'discre2zed'as'the'voxels'touched'while'traversing'the'volume'

‣ Path'integral'calcula2ons'are'independent'and'parallelize'at'the'level'of'protons'(but'inherently'sequen2al'within'each'path)



Linear solver (CARP)‣ The'result'of'MLP'is'a'system'of'equa2ons'rela2ng'each'proton’s'touched'

voxels'to'the'rela2ve'stopping'power'(roughly,'the'energy'loss)'‣ We'began'the'project'with'a'CPU'implementa2on'of'the'rowEac2on'based'

sparse'itera2ve'solver'CARP'(component'averaged'row'projec2ons)'‣ CARP'decomposes'the'matrix'into'row'blocks,'one'block'per'processor,'and'

iterates'to'sa2sfactory'convergence:'• Performs'a'JacobiElike'itera2on'sequen2ally'through'the'rows'to'produce'a'perE

block'solu2on'vector'• Averages'the'perEblock'solu2on'vectors'(in'componentEwise'fashion)'• Redistributes'the'solu2on'vector'x'to'all'processors

9



Hardware: Gaea GPU cluster at Northern Illinois University‣ 60'compute'nodes'‣ Node'configura2on'

• 2x'Intel'X5650'12Ecore'CPUs'• 2x'NVIDIA'M2070'GPUs'• 72GB'RAM'• QDR'Infiniband

10



Data decomposition‣ 2.1B'protons'/'60'nodes'=~'35M'protons'per'node'‣ 2'GPUs'E>'17M'protons'per'GPU'‣ The'maximum'voxels'per'proton'is'~364'‣ 17M'protons'x'364'voxels'x'4'bytes/voxel'='25GB'data'per'GPU'

• Larger'than'available'M2070'GPU'memory'of'6GB'‣ High'watermark'memory'requirement'on'cluster'is'3TB'(aggregate)

11



MLP (Most Likely Path) CUDA implementation‣ MLP'involves'calcula2ng'path'integral'of'the'protons'‣ Ini2al'implementa2on'assigns'a'thread'per'proton'‣ PerEGPU'proton'data'is'larger'than'GPU'memory'on'M2070'‣ Stage'batches'of'protons'to'GPU'‣ MLP'was'ported'to'the'GPU,'with'mul2ple'variants'

• gpu'struct:'Direct'port'of'CPUEbased'code'using'structured'proton/voxel'data'• gpu'flat'memory:'Flat'memory'space'with'perEproton'padded'voxel'arrays'• gpu'flat'memory'+'overlap:'Streaming'computa2on'to'overlap'compute'and'

hostEdevice'transfers'

12



MLP (Most Likely Path) CUDA implementation (26M protons, 2 GPUs)

13

Implementation Execution time (seconds) Speedup

cpu 598.7 -

gpu_struct 77.6 7.7x

gpu_flat_memory 55.5 10.8x

gpu_flat_memory + overlap 53.0 11.3x



Linear solver (CARP) CUDA implementation (26M protons, 2 GPUs)‣ CARP'ported'directly'from'CPU'code'‣ PerEnode'rowEblock'data'larger'than'GPU'memory;'batch'process'‣ Further'subdivide'perEnode'rowEblock'into'rowEblocks'per'streaming'mul2processor'

!

!

!

!

!

!

!

‣ Limited'speedup'in'GPU'implementa2on,'because:'• rowEac2on'based'solver'constrains'parallel'granularity'• scakered'memory'accesses'constrain'performance,'as'is'typical'of'sparse'matrix'opera2ons

14


cpu 161.0 -

gpu 139.3 1.16x



Performance at scale

15

Phase Execution time (seconds)

Setup 22.3

Most Likely Path (MLP) 151.0

Linear solver (CARP) 265.5

Overall execution time 438.8Initial goal was to complete in <600s (10mins)

2'billion'protons,'60'nodes,'12'CPU'cores/node,'2'GPUs/node



Further work: CARP Hybrid CPU/GPU‣ Assign'row'blocks'to'CPU'and'GPU'simultaneously'‣ Weighted'work'distribu2on'based'on'ini2al'performance'measurements

16


cpu 161.0 -

gpu 139.3 1.16x

hybrid 102.3 1.57x

2'billion'protons,'60'nodes,'12'cores/node,'2'GPUs/node



Future work‣ Integrate'alterna2ve'linear'solvers'to'improve'performance

(amgX,'cusparse,'PETSc)'‣ Consider'alternate'data'decomposi2ons'to'improve'cache'locality'

• volume'slab'per'streaming'mul2processor'• volume'wedge'per'streaming'mul2processor''

‣ Measure'performance'on'nextEgenera2on'GPUs'• K80'for'greater'performance'• Jetson/TK1'for'greater'performance/wak'

‣ Experiment'with'GPU'cloud'plauorms'(Amazon'cloud)

17



AcknowledgementsNicholas'T.'Karonis,'Northern'Illinois'University'(NIU)'and'Argonne'Na2onal'Laboratory'(ANL)'Michael'E.'Papka,'NIU'and'ANL'Caesar'Ordoñez,'NIU'Eric'Olson,'ANL'Kirk'Duffin,'NIU'Venkat'Vishwanath,'ANL'!

US'Department'of'Defense'contract'number'W81XWHE10E1E0170'sponsored'this'work.'

18


Accelerating Proton Computed Tomography with GPUson-demand.gputechconf.com/gtc/2015/presentation/S5497-Thomas-Uram.pdf · Accelerating Proton Computed Tomography with GPUs...

Documents