DANSE

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DANSE Distributed Data Analysis for Neutron
Scattering Experiments
Michael M. McKerns, Michael A.G. Aivazis, Tim M.
Kelley, June Kim, and Brent Fultz Materials
Science and Applied Physics California Institute
of Technology
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Abstract

• The DANSE system will merge the various
  computational tasks of neutron scattering into a
  unified, component based run-time environment.
  Standard components will implement data analysis,
  visualization, modeling, and instrument
  simulation for all areas of neutron scattering.
  A core technology of DANSE is an open source
  framework that supports the components and
  mediates their interactions. Within the DANSE
  environment, users will be able to mix and match
  different software components without
  compilation, and execute calculations seamlessly
  across distributed resources. DANSE will provide
  tools to help instrument scientists and expert
  users migrate their existing routines (written in
  any number of languages) to components, and an
  interface that will allow new and casual users to
  access a stock set of standard analysis
  applications or configure their own new computing
  procedures for novel experiments. The modular
  structure of DANSE parallels the steps of data
  analysis performed by scientists, thus making it
  a natural environment for creating flexible
  computing procedures. DANSE will lower barriers
  to sharing software, and extend the
  experimentalists toolkit with capabilities of
  analysis and interpretation such as
  high-performance simulations (band structure,
  molecular dynamics, etc.), co-analysis of data
  from multiple experiments, and real-time feedback
  for experimental control.

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An introduction to DANSE

• DANSE is a community organizing project with the
  potential to provide a unique facility/user
  interaction
• a single environment for data analysis,
  visualization, modeling, and instrument
  simulation for all areas of neutron scattering
• a collaborative effort between software
  professionals, neutron scattering scientists, and
  facilities
• provides tools for remote collaboration and
  co-analysis
• support from members of the international
  community and from the directors of SNS, IPNS,
  HFIR/CNS, Lujan Center, NCNR
• potentially the software environment for all
  instruments at the SNS
• DANSE provides a unified component-based runtime
  environment for computational neutron scattering
• open-source framework provides seamless use of
  distributed and high-performance resources
• a flexible, extensible, dynamic, interactive,
  cross-platform, cross-compiler, object-oriented
  software architecture
• integration of legacy codes and
  community-standard software
• well suited for the development of new science,
  standard stock computation, quality and
  plausibility assessment, and as a educational
  tool

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Tools for each level of user

• Beginning student
• user of prepackaged tools and documentation as a
  learning environment
• Visiting scientist
• user of prepackaged and specialized analysis
  tools
• Instrument scientist
• author of prepackaged specialized tools
• Analysis expert
• author of analysis, modeling or simulation
  software
• Established researcher
• collaboration coordinator, designer of new
  analysis procedures
• Software integrator
• responsible for extending software with new
  technology
• Framework maintainer
• responsible for maintaining and extending the
  DANSE infrastructure

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Encourages Better Science

• More science from experiment execution
• Single crystals on chopper spectrometers
• Feedback control for engineering diffraction
• Alter experiment depending on results
• visualization of science trends, not data trends
  e.g., see structure, not I(Q)
• on-demand modeling, ab-initio calculations
• reality checks against scattering theory
• Better science by planning experiments
• Plausibility tests before submitting a proposal
• Assessment of sample plus instrument
• Contingency planning using prior simulations
• Assessments of trends in previous data

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Facilitates New Science

• New science with better data analysis
• FEM calculations of strains in microstructures
• Monte-Carlo inversions of S(Q,E) to obtain
  parameters of structure and dynamics models
• Model refinements with multiple data sets.
• New science by leveraging theory
• VASP, CASTEP, ABINIT are commodities today use
  them for assessing structures and dynamics.
• Micromechanics correlations of local strains
• Phase diagrams thermodynamic functions
• Ab-initio calculations of spin interactions
• Soft matter structure atomic force fields
  guided by diffraction

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• Simulation and plausibility testing on virtual
  instruments

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Ni
Pd
Pt
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Phonon Partition Function fcc Ni
for E,g_E in spectrum Z one_osc(E,T)
g_E
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Built on the Pyre integration architecture

• Pyre is a robust, stable foundation
• 75,000 lines of Python 30,000 lines of C
• multiply leveraged DoE ASCI project
• Pyre is a software architecture
• a specification of the organization of the
  software system
• a description of the crucial structural elements
  and their interfaces
• a specification for the possible collaborations
  of these elements
• a strategy for the composition of structural and
  behavioral elements
• Pyre is multi-layered
• flexibility
• complexity management
• robustness under evolutionary pressures
• Pyre is a component framework

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Component architecture

• The integration framework is a set of
  co-operating abstract services

service
component
python
bindings
package
framework
custom code
facility
facility
facility
facility
extension
core
component
component
requirement
bindings
bindings
library
custom code
implementation
FORTRAN/C/C
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A Path for Software of Today
ANL
java
ISAW
NIST
IDL
DAVE
ISIS
Matlab
Mslice
LANL
F77
GSAS

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Component dataflow

• Granularity allows reusability of object-oriented
  components

instrument info
Selector
energy bins
filename
times
filename
Energy
NeXusWriter
NeXusReader
Selector
time interval
Bckgrnd
raw counts
Selector
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Data Flow Paradigm
Component Templates Standard
Data Streams
properties
histograms tables meta-data
Name Place
Code Place
Standard communication protocol between
components that can reside anywhere
Initiate, terminate, error
Python objects
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'''Multiphonon.py Calculates the multiphonon
scattering, using a phonon DOS... ''' from
mpFunctions import def run(All_Inputs_List)
"""Multiphonon.py main loop..."""
check user inputs for validity, get data from
disk checkUserInput(input_arglist)
setup_arglist setupRun(run_arglist)
1-phonon quantities, multiphonon terms
single_arglist onePhonon(arglist)
multi_arglist multiPhonon(N_arglist)
prepare results for output, send to disk, etc.
output_arglist prepareResults(result_arglist)
outputResults(output_arglist) return if
__name__ '__main__' """Run main loop if
launched standalone.""" from mpUserInput
import run(All_Inputs_List)
Encapsulation
Launched standalone or Inside Analysis Procedure
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Component implementation strategy

• Write engine
• custom code, third party libraries
• modularize by providing explicit support for life
  cycle management
• implement handling of exceptional events
• Construct python bindings
• select entry points to expose
• Integrate into framework
• construct object oriented veneer
• extend and leverage framework services
• Cast as a component
• provide object that implements component
  interface
• describe user configurable parameters
• provide meta data that specify the IO port
  characteristics
• code custom conversions from standard data
  streams into lower level data structures

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Flexibility through the use of scripting

• Scripting enables us to
• organize large numbers of user tunable parameters
• allow the runtime environment to discover new
  capabilities without the need for recompilation
  or relinking
• compose computations at runtime
• The interpretive environment
• Python is
• a modern object oriented language
• robust, portable, mature, well supported, well
  documented
• easily extensible
• rapid application development
• has been extended to support for parallel
  programming
• has no measurable impact on either performance or
  scalability

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Encapsulating critical technologies

• Extensibility
• new algorithms and analysis engines
• technologies and infrastructure
• High end
• visualization
• easy access to large data sets
• single runs, backgrounds, archived data
• metadata
• distributed computing
• parallel computing
• Flexibility
• interactivity web, GUI, scripts
• must be able to do almost everything on a laptop

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Data Analysis as a Distributed Service

• Data analysis is a service controlled by the
  user
• Users laptop issues commands and receives
  results
• Computation is arranged by your client software

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Support for distributed computing

• We are in the process of migrating the existing
  support for distributed processing into gsl, a
  new package that completely encapsulates the
  middleware
• Provide both user space and grid-enabled solution
• User space
• ssh, scp
• pyre service factories and component management
• Web services
• pyglobus
• Advanced features
• dynamic discovery for optimized deployment
• reservation system for computational resources

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Strengthening the neutron community
Billinge
Fultz/Trouw
Fultz/Aivazis
Ustundag
Butler
Kienzle
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3 SNS instruments on-line in 20062 IDT
instruments
Software needs to be on-line to support BL 2, 4a,
4b, 5, 18
Backscattering Spectrometer BL 2
Disordered Materials Diffractometer BL 1b
ARCS Spectrometer BL 18
PROTONS
High Resolution Chopper Spectrometer BL 17
High Pressure Diffractometer BL 3
Fundamental Physics Beamline BL 13
Magnetism BL 4a Liquids BL 4b Reflectometers
Cold Neutron Chopper Spectrometer BL 5
Single Crystal Diffractometer BL 12
SANS BL 6
Engineering Diffractometer BL 9
Powder Diffractometer BL 11a
Areas for User and Instrument Support
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