Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada NEP-02: Service Oriented Scientific Computing for SAFORAH – Non-linear Denoising Parallelization Demo Ashok Agarwal, Patrick Armstrong, Andre Charbonneau, Hao Chen, Ronald J. Desmarais, Ian Gable, David G. Goodenough, Piper Gordon, Aimin Guan, Roger Impey, Kelsey Lang, Belaid Moa, Susan Perkins, Wayne Podaima, Randall Sobie  February 15, 2011

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Road Map  Introduction Introduction to non-linear denoising Problem – algorithm is expensive Solution – parallelize  Non-linear denoising application How we parallelize using the grid?  Application work done during extension Non-linear Denoising application SAFORAH: CUDOS web interface  Future work

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Introduction  Hyperspectral noise has been assumed to be linear Linear Stochastic Processes were used to model the noise  Recently, Han and Goodenough (2009) have proved that this is not the case Non-linear analysis is used to reduce the noise Results show significant signal to noise ratio SNR boost:

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Introduction Original Image Difference Image Denoised Image Aviris Image (600x1000x179) RGB: 1503, 750, 645 nm Linear 2% trimming Non-linear Denoising Results:

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Introduction  Typical hyperspectral image cube 600 samples, 1000 lines, 179 bands Over 200 MB  Problem: The algorithm is computationally very expensive. This 200MB image will take approximately four days to process on a single cpu.  Solution: Give the algorithm more cpu’s!

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada   Solution: Give the algorithm more cpu’s: Parallelize using the computational grid.   Embarrassingly parallel problem: No spatial data interdependence Data can be divided spatially for processing Non-linear Denoising

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Non-linear Denoising   The image is split into N pieces using the gdal library:   Number of pieces based on image size.   For example, this sample image is broken into 250 pieces.

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Non-linear Denoising   Each piece is sent to the grid with the application source code and a script to compile and run it: Denoise.cpp Makefile Denoise.sh Computational grid Job # n

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Non-linear Denoise Computational grid Non-linear Denoising Job # 1 Job # 2 Job # 3 Job # … Job # n Metascheduler Resource 1 Resource 2 Resource m   The pieces are sent to the metascheduler The metascheduler allocates resources for the jobs:

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Non-linear Denoising Computational grid   The nodes copy the completed images back to the local machine The Non-linear Denoising application stitches the pieces together:

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada   Advantages:   Two orders of magnitude faster!   Completed in as little as 1 hour on a grid with 74 nodes, instead of 4 days, or about 100 hours.   The time to process dependent on: Size of image file Number of nodes available on the grid How many other jobs are running on the grid Non-linear Denoising

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada  The activities in this quarter dealt with making the parallel Non-linear Denoising (NLD) application available in the CUDOS GUI, and preparing the application and software infrastructure for longevity. Developed the parallel NLD application Improved stability Improved response time Made NLD available from CUDOS GUI Ran end-user testing for NLD  The infrastructure activities included: Improvements to source code version management Extension

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada 13 Changes to the Architecture Extension

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada System Flow Chart Results Pool of Computational Resources Splitter Scatterer Gatherer Job Scheduler Product Request Merger Gridway MetaschedulerGridway Metascheduler Portable Batch System (PBS)Portable Batch System (PBS) Extension

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Demo of Automated Parallelization of Denoising SAFORAH CUDOS

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Future Work   Update the other existing EO applications according to the user requirements on parallelization (Radarsat 2 decomposition, Aboveground carbon mapping)   Update Grid Web Integration Service to use supported APIs Globus 4.0 no longer supported Other possibilities: Condor Globus 5.x Cloud computing   Add more nodes to grid to improve performance. Make more applications available on OGC and on CUDOS

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Conclusion  Accomplished what we set out to do for extension: Parallelized Non-linear Denoising. Using grid-computing for speed and efficiency. Obtained significant performance improvements from parallelization: ~100 x faster!! Made the application available on SAFORAH Improved the system infrastructure  Thank you CANARIE!

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada 18 CUDOS Portal - Create New Product

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada 19 CUDOS Portal - Product Parameters

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada 20 CUDOS Portal - Product Order

Natural Resources Canada Ressources naturelles Canada Canadian Forest Service Service canadien des forêts Conseil national de recherches Canada National Research Council of Canada Results on noise reduction (1) The effectiveness of noise reduction by the LGP was evaluated by comparing the noise intensity and signal-to-noise ratio (SNR) between the original and the de-noised images. A covariance method proposed by Roger and Arnold (1996) was employed here for noise estimation. Without relying on any additional information, this method is capable of producing reasonable noise estimates based on the covariance matrix of the hyperspectral image alone. The average noise intensity of the original AVIRIS image was estimated at 1.2, which varies between 0.3 (minimum) and 11.3 (maximum) across all bands. The LGP removed an average of 34.4% of the noise in the AVIRIS image, which left the noise intensity in the post-denoised image varying between 0.2 and 9.0 with an average of 0.9.