Richard Dorrance November 4, 2011

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High Speed 3D Tomographyon CPU, GPU, and FPGA

Nicolas GAC, Stéphane Mancini, Michel Desvignes, Dominique Houzet

Reconfigurable MPSoC versus GPU:Performance, Power and Energy Evaluation

Diana Göhringer, Matthias Birk, Yves Dasse-Tiyo,Nicole Ruiter, Michael Hübner, Jürgen Becker

Richard DorranceNovember 4, 2011

Literature Review

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Review

Computed Tomography

Tomography

Basis for CAT scan, MRI, PET, SPECT, etc.

Cross-sectional imagingtechnique using transmissionor reflection data frommultiple angles

Computed Tomography (CT):A form of tomographic reconstruction on computers

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Cross-Sections by X-Ray Projections

Project X-ray through biological tissue;measure total absorption of ray by tissue

Projection Pθ(t) is the Radontransform of object functionf(x,y):

Total set of projections calledsinogram

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, cos sinP t f x y x y t dxdy

Phantom and Sinogram

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Shepp-Logan Phantom

CT Reconstruction

Restore image from projection data

Inverse Radon transform

Most common algorithm is filtered backprojection– “Smear” each projection over image plane

Accuracy of reconstruction depends on the number of detectors and projection angles

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Original 4 Angles 16 Angles 64 Angles 256 Angles

Note on Filtering

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No Filtering With Filtering

FBP Algorithm

Input: sinogram sino(θ, N) Output: image img(x,y)

for each θfilter sino(θ,*)for each x

for each yn = x cos θ + y sin θimg(x,y) = sino(θ, n) + img(x,y)

O(N3) algorithm– But highly parallelizable, given sufficient memory

bandwidth; not computationally intensive

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High Speed 3D Tomographyon CPU, GPU, and FPGA

Nicolas GAC, Stéphane Mancini, Michel Desvignes, Dominique Houzet

3PA-PET (Pipelined, Prefetch, Parallelized)

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Algorithms

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Hardware

CPU– Desktop PC: Pentium 4 (3.2 GHz)– Workstation: bi-Xeon Dual Core (3.0 GHz)

GPU– Nvidia GeForce 8800 GTS (1.2 GHz, 96 Cores)

FPGA– Virtex 4 (200 MHz)

ASIC– Projected/Extrapolated (1.2 GHz)

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CPU vs. GPU vs. FPGA vs. ASIC

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w/ Proper Normalization

Hardware Algorithm # of PE [cycles/px] [cycles/px*PE]

Pentium 4 STIR 1 34,505.21 34,505.21

Pentium 4 VBI-flt(v1) 1 169,580.85 169,580.85

Pentium 4 VBI-flt(v2) 1 53,943.45 53,943.45

Pentium 4 VBI-flt(v3) 1 7,750.50 7,750.50

Xeon (Dual Core) STIR 1 16,682.94 16,682.94

Xeon (Dual Core) VBI-flt(v3) 1 3,400.53 3,400.53

Xeon (Dual Core) VBI-flt(v3) 2 1,694.45 3,388.90

Xeon (Dual Core) VBI-flt(v3) 4 854.49 3,417.97

GPU VBI-flt(v4) 96 115.09 11,049.11

GPU VBI-flt(v5) 96 58.13 5,580.36

FPGA VBI-fix 1 484.41 484.41

FPGA VBI-fix 4 149.97 599.89

FPGA VBI-fix 8 101.92 815.35

ASIC VBI-fix 1 580.12 580.12

ASIC VBI-fix 4 248.79 995.16

ASIC VBI-fix 8 156.95 1,255.58

ASIC VBI-fix 40 31.39 1,255.58

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Reconfigurable MPSoC versus GPU:Performance, Power and Energy Evaluation

Diana Göhringer, Matthias Birk, Yves Dasse-Tiyo,Nicole Ruiter, Michael Hübner, Jürgen Becker

RAMPSoC

Runtime adaptive multi-processor system-on-chip– ROACH/iBOB-like system from a group out of Germany

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3D Ultrasound Computed Tomography

Mammography for earlybreast cancer detection

3D USCT works on thesame principles asregular CT scans

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Hardware

CPU– AMD Athlon 64 3200+ (2.2 GHz, 1 GB RAM)

GPU– Nvidia Tesla C2050 (1.15 GHz, 448 Cores)

FPGA– Xilinx Virtex-4FX100 (125 MHz)

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CPU vs. GPU vs. FPGA

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Hardware # of PE [cycles/img] [cycles/img*PE] [W] [1/J]

Athlon 64 1 330,000.00 330,000.00 177 37

GPU 448 3,714.50 1,664,096.00 270 1147

FPGA 8 18,000.00 144,000.00 3.61 1924

References

1. N. GAC, et al., “High Speed 3D Tomography on CPU, GPU, and FPGA,” EURASIP Journal on Embedded Systems, vol. 2008, Article ID 930250, 12 pages, 2008.

2. D. Göhringer, et al., “Reconfigurable MPSoC versus GPU: Performance, power and energy evaluation,” INDIN‘11, pp.848-853, 26-29 July 2011.

3. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging, IEEE Press, 1988.

4. J. Hsieh, Computerized Tomography: Principles, Design, Artifacts, and Recent Advancements, SPIE & Wiley, 2009.

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