1. Brent Fultz California Institute of Technology
DANSE
Brent Fultz
California Institute of Technology
ARCS Software Project
Distributed Data Analysis for Neutron Scattering Experiments
Components and Data Streams
Towards a National Project

2. ARCS Spectrometer Moderator Shutter Guides Choppers Sample
Introduce self
Point out picture
Familiarize you with the different components
Hope that you’ll be able to see the logic that went into some of the decisions
Detector Array
Beamstop

3. Scientific Software for Neutron Scattering
Software enables science from an inelastic neutron spectrometer such as ARCS
Some experiments are impossible owing to present software
Many experiments produce better science with better software (optimize beamtime usage, experimental procedures tuned on-the-fly)
New opportunities to connect to theory of materials

4. Hardware Project Schedule

5. Software Development

6. Software Project Schedule

7. Software Project Milestones
Software Baseline Design Jan. 2003
Software First Build July 2004
Software Beta Release Mar. 2005
Software Release Feb. 2006
End ARCS Project Sept. 2006

8. Software Roadmap v. 1.0

9. Data Reduction Account for incident flux Remove background
Convert from time to energy
Correct for detector efficiency
Bin into rings of constant scattering angle
Convert from angle to momentum
Subtract multiphonon and multiple scattering
Correct for absorption

10. Architectural Coherence
Left: Saint-Sernin, Toulouse, Romanesque Pilgrimage Church, c
Right: Notre-Dame, Amiens, French Gothic Cathedral, begun 1220
Inconsistencies in Architecture
Cathedrals --- Charming
Software --- Annoying

11. Enforce Coherence -- Write the Manual Now
Define needs and scope
Reference for developers
Identify and show interrelationships
Standardize notation
Documentation
Experimental Inelastic Neutron Scattering Theory of neutron scattering Theory of excitations in condensed matter Data analysis procedures Software architecture Reference manual

12. Key Concept: Data Analysis as a Web Service
Data analysis is a service
controlled by user
Computation is arranged by the web server
user’s laptop issues commands
and receives results

13. Key Concept: Data Analysis as a Web Service
XML-RPC is an open standard for remote procedure calls
The user’s web browser issues commands to a server
The server distributes the work to appropriate computers

14. Early User Interface (Browser/Java)
Users navigate the web site with the left frame (content is generated dynamically by the web server).
Users select data and calculational tools, and add them to the Java applet on the right (like “Labview”)
Mention: Authorized users can continually add new transformations or data sets, and since the content is dynamically generated HTML, these new items are immediately available to all users.

15. The User Interface (Python/Viper/Cobra)
The user “wires” the boxes together to represent data flows
We intend to allow users to insert new code in empty boxes, and archive sessions and procedures

16. Data Analysis Execution
User hits “Run”
GUI translates wiring diagram into XML-RPC commands
Server receives commands, arranges Python script, starts data processing.
This allows us to have a central repository of data and computing resources which many users can share. The users themselves do not need powerful machines or fast internet connections to process their data.

17. Big Concept of a Web Service
The server can provide access to the best combination of hardware and software
Experimental data and analysis codes reside on the servers, so little data bandwidth is needed
Computing resources can be changed without affecting the user
Computation can be local or non-local
Clean separation of GUI from analysis code
One web portal for all neutron instruments(?)

18. The Bigger Concept Underneath
Components
Pre-compiled Python objects called and re-arranged by the Python Interpreter
Data Streams
Standard communication protocol between components
Note: Standard streams can connect components located anywhere…

19. Tools for Programmers
Component Templates Standard Data Streams

20. Component

21. Rebinner

22. Levels of Code Development
For using existing scripts, entry barrier nearly zero.
For altering existing Python scripts, entry barrier is very low.
For writing new Python code, entry barrier is modest. Performance may be comparable to IDL or Matlab.
Transition to high performance compiled code
ARCS: Writing Python bindings for C++
DANSE: Component templates for C++, FORTRAN, Java?

23. Data Rebinning – Tim’s Test
Tested t to E rebin computation in several forms.
300 seconds with IDL
60 seconds with IDL using a DLL compiled from C++
2 seconds with C++ “Rebinner” class

24. GUI form/function

25. Extension Beyond ARCS Science Large-scale structures Diffraction
Engineering diffraction
Inelastic scattering
Theory of structure and dynamics
Neutronics and nuclear physics
Engineering
Software Tools
Hardware Tools

26. Management Structure User Base (Help with Requirements and Testing)
Virtual Instrument Development Team
User Opinions and Testing
Executive Committee (Direct DANSE)
Subproject Leaders
Meetings Every 3 Months, Later 6 Months
Steering Committee (Institutional Issues)
Facilities Directors
Caltech Provost’s Office

27. Money Is Helpful Software tools need development (people)
Hardware for multiple users (people and machines)
Software subprojects at facilities must have DANSE funding
Five-Year Budget
20 M$ 5 subprojects M$/y
10 M$ Central Resources 7 FTE + hardware
30 M$

28. Agenda
Morning
8:30 Fultz: Overview of ARCS software project, and its extension to other neutron science.
9 Lin and Kelley: Demo and commentary on distributed data analysis.
9:20 Aivazis: Overview of the DANSE architecture, and how it fits into future computing infrastructures.
10 AM: Dan Meiron (Vice-Provost for Computing): Large-scale scientific computing at Caltech
Break
Discussion of topics such as:
Needs of facilities, Needs of Caltech, Needs of DANSE project,
Management of DANSE project, Organization of subprojects, Proposal
Lunch
Afternoon
Further discussion of DANSE management and subproject organization, white paper revisions
Dinner
7 PM Café Santorini, Old Pasadena

29. End of Presentation

30. Software Roadmap v. 1.0

31. Born - von Kármán Lattice Dynamics
Simplicity: Complexity:
Undergrowth of indices for tensor quantities:
Crystal structure:
lattice, l, basis, k
allowed elements
depend on symmetry

32. J. M. Ziman, Electrons and Phonons

33. Electronic Publishing
library
documentation ? ? ?
user
codes
server
instrument
and data

34. Software Survey 10 responses, from serious users.
very detailed comments.

35. User Ratings of Software Packages
User Opinion (–2 to +2)
Strength of Opinion (0 to +3)
Number of Respondents
Rating = UO*SO/N

36. Software Survey Results
Interesting:
CS: ISAW, Unisoft, Computational Crystallographic Toolkit
User: ISAW, DAVE, Mslice, McStas, Gulp, Chop
Problems:
Code Quality and User Opinion are
not necessarily correlated (McStas is an exception)
2. We are still looking at details, but we can already see that there will be more rewriting than expected

37. Issues in Code Development
X hours to develop a code for yourself
3 X hours to make it convenient for others
9 X hours to make it run for others on several platforms
Adding personnel to a late software project makes the project later. (Brooks Law)