High performance computing in data and image processing A.A. Lukianitsa 1,2, A.G. Shishkin 1,2, F.S. Zaitsev 1,2 1 Fusion Advanced Research Group Ltd., Slovakia. 2 Moscow State University, Russia th Workshop on Fusion Data Processing Validation and Analysis Invitation. Frascati 2012

Contents 1.Motivation. 2.Previous experience in fusion data mining. 3.High performance computing (HPC). 4.Examples of HPC applications. 5.“Virtual Tokamak” technology. 6.Summary. 2

1. Motivation Huge amounts of collected and stored data from various sources: –Large physical experiments. –Video surveillance. –DNA sequencing. New challenges in various scientific areas: –Real-time thermonuclear pulses control. –Genomic sequence analysis and gene-finding. –Real-time video analysis and indexing. New computational algorithms. Internet transmission speeds and usage keep growing. Cloud computing. Urgent need for high performance computing. 3

L,H-mode classification with SVM for the Global confinement database 10 4 pulses [Plas.Phys.Contr. Fus. 50(2008)065013]. 2. Previous experience in fusion data mining Linear separating hyperplane was found Forecast accuracy %. Deep H-mode is predicted for ITER. Power loss threshold is more optimistic for ITER ~0 (was ~40) MW. Allows checking L or H-mode for a particular set of 8 parameters, including prediction and evaluation how deep is the set in a mode. 4

Code VIP (Video Image Processing). Plasma boundary reconstruction in (R,Z,η). Dynamic programming algorithm. [32 EPS Conf. on Plasma Phys. 2005, P ] 5

Actual plasma boundary is quite different from the image and can be different from the EFIT reconstruction. Code VIP was installed in MAST for regular use. 6

3D processing: plasma boundary reconstruction. 7

Detection and tracking of snow flakes in JET plasma video. [Frontiers in Diagnostic Technologies.1st Int. Conf. Frascati 2009] Complicated algorithm: camera image stabilizing, image intensity equa- lizing, background modeling, foreground particles separation, snowflakes tracking Frame=78Frame=79Frame=80 Snowflakes tracking over the time Trajectories 8

Demo of snow flakes detecting and tracking. 9 The moment of appearance, trajectory, size, shape and color can give understanding what is happening.

Code FIRe (Fluorescence Intensity Reconstruction) - reconstructing distribution of the source of light using high resolution photo or video images. [1st Korean-Russian Workshop on Data Mining. 2007, p ] Tikhonov regularization algorithm. Treating several cameras. Removing the lens distortions. Options for given number of mirror or diffusive reflections from surfaces. Ray tracing. Description of each particular surface using triangular grid. Reconstruction for MAST plasmas One camera in the equatorial plane 10

Processing of magnetic diagnostics data using Hidden Markov Models (HMMs). Integrated data analyses. [8th Int. FLINS Conference "Computational Intelligence in Decision and Control“. Madrid 2008, p ] Well suited, since magnetic frequencies are similar to the speech ones. HMMs allow compression (from giga to kilo bytes) of time dependent data of different length, without substantial loss of information, to a standardized form, which can be used for different purposes, e.g. pattern recognition. Possible applications (almost all plasma processes change magnetic field): Navigation in the magnetic databases: finding similar or distant patterns. Instabilities recognition and prediction. Disruption prediction. Recognition of L, H-transition during a hybrid scenario pulse. Regular oscillations recognition. Determination of symmetry or rotation characteristics in poloidal or toroidal angles, harmonics numbers, unstable modes. Integrated analyses of data from different diagnostics (~100 in JET). Correlating with experimental (X-ray, optical, neutral, neutron, currents in coils, etc.) and/or numerical (safety factor, current density, type of instability, plasma shape, temperature and density profiles, etc). 11

Transformation of magnetic oscillations to the sound frequencies. 12

3. HP computing CPU vs. GPU: CPU –Fast caches. –Branching adaptability. –High performance. GPU –Multiple ALUs (Arithmetic Logic Units). –Fast onboard memory. –High throughput on parallel tasks/ Executes program on each fragment/vertex. –Easily scalable. CPUs are good for task parallelism. GPUs are good for data parallelism. 13

FARG experience with CUDA for commercial applications: Acceleration 14 log 10 S detecting & tracking of moving or left objects, monitoring car parking spaces, face detection & recognition, smoke & fire detection in videos, noise auto suppression, genetic information transfer, art authentication, emotion recognition by speech, biomedical signal analyses, etc.

4. Examples of HPC applications Demo of hyperplane construction with SVM. Accelertion ~

Key Features –Move to real-time Optical Flow on high video resolution and high frame rate. –Edge-preserving post processing algorithm to enhance quality. –More accurate occlusion estimation. –Scalable, modular, versatile architecture. –Low system’s resources consumption. Real-time НPC optical flow processing

GPU/CPU speed up, times Frame resolution Real-time НPC optical flow processing

Real-time НPC optical flow processing: the original video, background, blobs (compact moving objects), foreground (isolated blobs), blobs boundaries. 18

5. Virtual Tokamak Modeling plasma pulses and diagnostics within the universal toolbox “Virtual Tokamak” framework. Singling out the most advanced and adequate approaches and algorithms in direct and inverse problems in fusion. Their modification and standardization. Allowing remote usage of the software by a usual Web-browser. Distributed computations, cloud and GRID-like technologies. Integrated data analyses. Diagnostics benchmarking and co- ordination. Design support. Pilot URL: leader.ic.msu.su/~fusion 19

Demo of the “Vertual Tokamak” GUI. 20

6. Summary Accurate and robust data and image processing codes are described with HPC advancement of the algorithms HPC algorithms can allow real time plasma data analyses, reconstruction, modeling and 3D processing as in commercial applications. Possible application in advanced real-time feed-back plasma shape and position control and instabilities suppression. Advancement of the plasma and chamber state alarm systems. A reasonable direction of diagnostics advancements is application of FARG’s commercial HPC developments as modules in the existing software, e.g. used at JET. CUDA HPC has high potential in numerical codes: e-nets, Monte- Carlo. 21