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Cluster Programming Technology and its Application in Meteorology

Cluster Programming Technology and its Application in Meteorology. Computer and Automation Research Institute Hungarian Academy of Sciences. Hungarian Meteorological Service. Silicon Computers Ltd. Background.

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Cluster Programming Technology and its Application in Meteorology

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  1. Cluster Programming Technology and its Application in Meteorology Computer and AutomationResearch InstituteHungarian Academy of Sciences HungarianMeteorologicalService Silicon Computers Ltd.

  2. Background • Hungarian Meteorological ServicedevelopedMEANDER (MEsoscale Analysis Nowcasting and DEcision Routines) • crucial task in the protection of life and property (storm warning at Lake Balaton, weather warnings for aviation...) • based on incoming meteorology information and computational intensive methods • MTA SZTAKI developed P-GRADE parallel programming environment • efficient, graphical support for the entire life cycle of parallel program development • Cluster programming technology and its applications in meteorology • joint project of MTA SZTAKI, Hungarian Meteorological Serviceand Silicon Computers Ltd. • supported by Research & Development Division, Ministry of Education

  3. MEANDER Program Package • Goal: Analysis of all the available meteorology information • producing parameters on a high resolution regular mesh (10km--> 1km) • ultra-short range forecast (up to 6 hours) • Application: Forecasting dangerous weather situations (storms, fog, etc.) • Meteorology information: surface level measurements, high-altitude measurements, radar, satellite, lightning, results of previous computed models, etc. • Basic parameters: pressure, temperature, humidity, wind, … • Derived parameters: type of clouds, visibility, …

  4. Structure of MEANDER First guess data ALADDIN SYNOP data Satellitedata Radar data Lightning CANARI DELTA analysis decode Basic fields: pressure, temperature, humidity, wind Radar to grid Rainfall phase Satellite to grid Derived fields: Type of clouds, visibility, etc. Visibility Overcast BASIC GRID Type of clouds Visualization “Present” weather For meteorologist:HAWK For users: GIF

  5. Processing of satellite images Receiving image Satellite raw image 36000 km Transformation & Interpolation & Processing Processing:altitude of clouds Transformation Interpolation to basic GRID

  6. THE PROBLEM MEANDER Parallelversion PC clasterSGI Origin 2000 SUN E10000 Parallelisation? MEANDER Sequentialcode C, C++ Fortran Debugging? Performance?

  7. The solution: P-GRADE development environment GRP_LBload balancer PROVE performancevisualizationtool GRED graphicaleditor TLC modelchecker GRP_MMmigration module Macrostep debugger GRP_CHKPT GRMmonitor DIWIDE distributed checkpointingtool GRAPNELgraphical language/ GRP2C pre- compiler distributeddebugger PVM or MPI message passing library Unix-like operating systems &C, C++, Fortran programming libraries

  8. 2D analysis MEANDER: 3D FIELDS Results: temperature and wind at 850 hPa level Radardata Satellitedata Delta 3D analysis ...computes the basic meteorological fields: pressure, temperature, humidity, wind velocity and direction for a high resolution 3D mesh (10km -1km)

  9. Implementation of DELTA analysis in P-GRADE Fortran seq. code

  10. P-GRADE version of MEANDER for clusters & supercomputers 25 x 25 x 4 x 25 x 10 x 20 x

  11. Live demo (5th DataGRID conference) P-GRADE PERL-GRID 11/5 Mbit Dedicated job ftp.met.hu netCDF 34 Mbit Shared job netCDF input GRM TRACE & Results GRM TRACE &Results 512 kbit Shared PERL-GRID CONDOR-PVM Parallel execution and GRM

  12. PROVE performance visualization

  13. Resource requirements of P-GRADE Edit, debugging Performance-analysis Testing,Execution

  14. Advantages of P-GRADE environment • Efficient support for each stage of parallel program development • Fast parallelisation of existing algorithms • Reusability of sequential code • Hiding of low level communication functions • Unified and integrated graphical concept • Predefined communication templates • Support for hierarchical design • Even non-professional programmers can use it (steep learning curve) • Portability from supercomputers to PC clusters

  15. Support for cluster computing III. Load balancing& migration • New facilities in P-GRADEfor long-running parallelapplications: • Distributed checkpointing • Process migration • Dynamic load balancing • Fault-tolerance execution More information: www.lpds.sztaki.hu I. Execution withoutload balancing IV. Result:Faster execution II. Checkpointing

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