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What’s LabVIEW and how it works?
The parallel computing in LabVIEW
What applications which LabVIEW and GPUs
being a fit? Case study Gallery
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What is LabVIEW?
The graphical, dataflow programming language
provides a better way for you to solve problems
than traditional, lowerlevel alternatives, and the
proof is in its longevity.
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Integrate with LabVIEW
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FFT
Interpolation
LabVIEW
LabVIEW
What applications which LabVIEW and GPUs
being a fit? People who need…
FFT calculation in realtime
massively parallel tasks heavy dsp algorithms
Just like… Multichannel audio analysis
OCT (Optical Coherence Tomography)
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D. D. Sampson, T. R. Hillman, Optical coherence tomography, Lasers
and Current Optical Techniques in Biology, G. Palumbo and R.
Pratesi, eds. (ESP Comprehensive Series in Photosciences,
Cambridge, UK, 2004), pp. 481-571.
Case study #1 Using NI FlexRIO
to Develop a HighSpeed, Compact OCT
Imaging System
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In an OCT system, obtaining the final
image requires significant processing
including fast Fourier transforms
(FFTs), interpolation, and DC offset
calculations.
Case study #2 GPU1: GTX 580
512 stream processors, 1.59GHz
processor clock and 1.5 GBytes
graphics memory
GPU2: GTS 450
with 192 stream processors, 1.76GHz
processor clock and 1.0 GBytes
graphics memory is dedicated for the
volume rendering and display of the
complete Cscan data
The GPU is programmed through
NVIDIA’s Compute Unified Device
Architecture (CUDA) technology. The
software is developed under the
Microsoft Visual C + + environment
with National Instrument’s IMAQ
Win32 APIs.
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Zhang, K. (2011). dx.doi.org/10.1364/BOE.2.000764
Signal processing flow chart of
the dualGPUs architecture.
Dashed arrows, thread
triggering; Solid arrows, main
data stream; Hollow arrows,
internal data flow of the GPU.
Here the graphics memory
refers to global memory.
The signal processing flow chart
of the dualGPUs architecture is
illustrated in Fig. 2, where three
major threads are used for the
FDOCT system raw data
acquisition (Thread 1), the GPU
accelerated FDOCT data
processing (Thread 2), and the
GPU based volume rendering
(Thread 3).
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In vivo human finger nail
fold imaging: (a)~(d) are
rendered from the same 3D
data set with different view
angles. The green
arrows/dots on each 2D
frame correspond to the
same edges/ vertexes of the
rendering volume frame.
Volume size: 256(Y) × 100(X) ×
1024(Z) voxels/ 3.5mm (Y) ×
3.5mm (X) × 3mm (Z).
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