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Monte Carlo Instrument Simulation Activity at ISIS Dickon Champion, ISIS Facility.

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Presentation on theme: "Monte Carlo Instrument Simulation Activity at ISIS Dickon Champion, ISIS Facility."— Presentation transcript:

1 Monte Carlo Instrument Simulation Activity at ISIS Dickon Champion, ISIS Facility

2 HET Fermi Chopper Simulation

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4 L1L2 m=2 (10cmx10cm) m=3.0 (2cmx4cm) OSIRIS Back Scattering Instrument

5 Wish Diffractometer Guide

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7 Polarizing Mirror Component Single bounce 0.4° 1.2°

8 Double bounce 0.4° 1.2°

9 New Vitess Module for ISIS

10 Distributed Monte Carlo Instrument Simulations

11 What is Distributed Computing The software we use VITESS Specifics McStas Specifics Conclusions Introduction

12 What do I mean by ‘Distributed Grid’? A way of speeding up large, compute intensive tasks Break large jobs into smaller chunks Send these chunks out to (distributed) machines Distributed machines do the work Collate and merge the results

13 Spare Cycles Concept Typical PC usage is about 10% Most PCs not used at all after 5pm Even with ‘heavily used’ (Outlook, Word, IE) PCs, the CPU is still grossly underutilised Everyone wants a fast PC! Can we use (“steal?”) their unused CPU cycles? SETI@home, World Community Grid (www.worldcommunitygrid.org)www.worldcommunitygrid.org

14 Visual Introduction to the Grid

15 CPU Intensive Low to moderate memory use Not too much file output Coarse grained Command line / batch driven Licensing issues? Suitable / Unsuitable Applications

16 Two scenarios: Single large simulation run Split the neutrons into smaller numbers and execute separately Merge results in some way Many smaller runs Parameter scan Monte Carlo Speed-up Ideas

17 Easy mode of operation: fixed executables + data files Executables held on server Split command line into bits – divide Ncount Vary the random seed Create data packages Upload data packages VITESS – Splitting It

18 Use GUI to create instrument – Save As Command “Parameter directory” set to “.” VITESS – Running It Submit program parses bat file Substitutes ‘V’ and ‘P’ Removes ‘header’ and ‘footer’ Creates many new bat files with different ‘--Z’s and

19 Submit program creates many bat files VITESS – Running It C:\My_GRID\VITESSE\VITESSE\build>Vitess-Submit.exe example_job example.bat req_files 20 logging in to https://bruce.nd.rl.ac.uk:18443/mgsi/rpc_soap.fcgi as tom.... Adding Vitesse dataset.... Adding Vitesse datas.... 3e+007 neutrons split into 20 chunks, of -n1500000 neutrons Total number of Vitesse 'runs' = 20 Uploading data for run #1... Uploading data for run #2.... Uploading data for run #19... Uploading data for run #20... Adding Vitesse datas to system.... Adding job.... Adding jobstep.... Turning on automatic workunit generation.... Closing jobstep.... All done Your job_id is 4878

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21 Download the ‘chunks’ Merge Data files DetectedNeutrons.dat : concatenate vpipes : trajectories & count rate Two classes of files 1D - Values: sum & divide by num chunks- - Errors: square, sum and divide 2D –Sum / num of chunks VITESS – Merging It

22 Many times faster: linear increase Needs verification runs (x3) Typically 11 (potentially) 30+ times faster 12 hours runs in 1 hour! Very large simulations reach random limits VITESS – Advantages and Problems

23 VITESS – Some Results 176 hours 59 hours 6hrs 20mins

24 Different executable for every run Executable must be uploaded at run time Split –n into chunks or run many instances (parameter scan) Create data (+ executable) packages Upload packages McStas – Splitting It

25 Use McGui to create and compile executable Create input file for Submit program McStas – Running It

26 Large run Submit program breaks up –n##### Uploads new command line + data + executable Parameter Scan Send each run to a separate machine McStas – Running It

27 Many output files  Separate merge program PGPLOT and Matlab implemented Very similar PGPLOT 1D – intensities: sum and divide. Errors: square, sum and divide. Events: Sum 2D – intensities: sum and divide. Errors: square, sum and divide. Events: Sum Matlab 1D – Same maths, different format 2D – Virtually the same ‘Metadata’ leave untouched McStas – Merging It

28 Security: Do we trust users? 100 times faster[?] Linux version much faster than Windows [?] How do we merge certain fields? values = '1.44156e+006 10459.9 30748'; statistics = 'X0=3.5418; dX=1.52975; Y0=0.000822474; dY=1.0288;'; Some issue related to randomness of moderator file McStas – Advantages and Problems

29 Both run well under Grid MP Submit & Retrieve a few hours work Merge a bit more Needs to merge more output formats [?] Issues with very large simulations More info on Grid MP at www.ud.comwww.ud.com Conclusions

30 Tom Griffin - GRID Ed Abel -GRID Stuart Ansell - MCNPX Mark Telling - OSIRIS Robert Dalgliesh - Polarization Laurent Chapon - WISH Judith Peters - HET Heloisa Bordallo - HET Geza Zsigmond -HET Acknowledgements


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