Advanced Geospatial Image Processing using Graphics Processing Units
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1 Advanced Geospatial Image Processing using Graphics Processing Units Atle Borsholm Ron Kneusel Exelis Visual Information Solutions Boulder, CO USA
2 Why? Common geospatial image processing algorithms are computationally demanding. Geospatial images are large and becoming larger. Sizes of 60,000 pixels on a side are not uncommon. GPU implementations of key geospatial processing algorithms can substantially offset this increase in calculation time. Users of our commercial products benefit from an environment that makes our tools easily extensible via the GPU.
3 AMPE Advanced Massively Parallel Execution Three tiers integrated with ENVI and IDL: Geospatial GPU versions of key algorithms for common computationally intensive processing provided out of the box. Image Processing GPU versions of common processing routines provided out of the box. User-Defined Kernels A sophisticated integration layer allows advanced users the ability to define, compile, and link custom GPU kernels within ENVI and IDL.
4 ENVI and IDL AMPE is fully integrated with and extends ENVI and IDL
5 AMPE - Geospatial Algorithms Orthorectification Atmospheric Correction (using QUAC) Principal Component Analysis (PCA) Adaptive Coherence Estimator (ACE)
6 AMPE Image Processing Interpolation multiple algorithms from nearest-neighbor to Lanczos Array operations min/max, mean, rescale Analysis - joint histogram, mutual information,cross-correlation, eigenvectors
7 AMPE Testing Environment All tests were performed on the following hardware: CPU: Dell, six cores, 3.47 GHz Xeon, 12 GB RAM, Ubuntu GPU: CUDA 6.5, NVIDIA Tesla K40 N.B. CPU tests used optimized code in ENVI and IDL which itself was often multithreaded.
8 AMPE Performance (Geospatial) CPU GPU Improvement Orthorectification x Atmospheric Correction x Principal Components x Adaptive Coherence Estimator x (time in seconds, average over multiple runs)
9 AMPE Performance (Interpolation) CPU GPU Improvement Nearest-Neighbor x Bilinear x Bicubic x Lagrange x (time in seconds, average over multiple runs)
10 AMPE User-defined Kernels Calculate the normalized difference vegetation index (NDVI): NDVI = (NIR VIS) / (NIR + VIS) Where: NIR = Near infrared band VIS = Visible band (red, ~675 nm)
11 AMPE User-defined Kernels extern "C" { global void k_ndvi(short *NIR, short *VIS, float *result, int n) { int i = blockidx.x * blockdim.x + threadidx.x; for ( ; i < n; i += blockdim.x * griddim.x) { result[i] = ((float) NIR[i] - VIS[i]) / (NIR[i] + VIS[i]); } } } - Kernel code in a separate.cu file (will change in future versions) - Call ampe_build_kernel() from running ENVI/IDL session - Will compile the kernel and build auto-generated IDL code (next slide)
12 AMPE User-defined Kernels function ampe::ndvi, NIR, VIS compile_opt idl2, logical_predicate ; parameter validation... ; allocate output device memory, float array output = ampe_makearray(nir.dim, TYPE=4) ; define "C" entry point entry = 'k_ndvi' module = 'AMPE_NDVI' + self.bitstring ; set up grid ng = NIR.n_elts / (NIR.n_elts mod nt gt 0) < ; call kernel ampe_run, NIR, VIS, output, NIR.n_elts, THREAD_X=nt, GRID_X=ng, $ FUNC=entry, MODULE=module, CALLBACK=self.oKernel end return, output
13 AMPE User-defined Kernels IDL> d_nir = ampe_put(nir) IDL> d_vis = ampe_put(vis) IDL> d_ndvi = ampe.ndvi(d_nir, d_vis) IDL> ndvi = ampe_get(d_ndvi) - IDL> is the interactive IDL prompt - NIR and VIS contain image bands on the CPU - ampe_put() places the image bands on the GPU - ampe.ndvi() calls a new method off the ampe object - ampe_get() returns the NDVI array from the GPU
14 AMPE is... Sophisticated geospatial analysis algorithms and common image processing routines combined with a framework for integrating user-defined kernels in order to maximize performance using GPUs.
15 Thank you! Atle Borsholm Ron Kneusel Website Please complete the Presenter Evaluation sent to you by or through the GTC Mobile App. Your feedback is important!
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