Difference between revisions of "Developers:Workshops:Evaluation:Meeting Notes"

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(November 3rd, Morning)
m (Brainstorming on Monte Carlo file format)
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* Presentation on Dataspork (http://www.mcc.uiuc.edu/dataspork/) by Jeongnim Kim
* Presentation on Dataspork (http://www.mcc.uiuc.edu/dataspork/) by Jeongnim Kim
** Download jar file: http://www.mcc.uiuc.edu/downloads_public/dataspork.1.1.jar

Revision as of 13:09, 3 November 2008

November 1st, Morning

What do we need in ALPS2 RC1

  • ALEA (Key Component for APLS2)
    • Clean Up
    • Replace recording part with boost
    • Accumulators (mean, median, statistics, errors)
    • Serialization (storgae to file)
    • Combining Data from several runs
  • RNG (brigitte's & boost) [discuss in afternoon]
  • Parameter (later with expressions) [Lukas]
  • Lattice Implementations [copy from alps 1]
  • Scheduler [second priority]
  • XML Support (Wrapper for some library) [Lukas]
  • Hierarchical Structure for Parameters [discuss on Monday]]

Discuss in the Afternoon

  • RNG
  • CMake
  • ALEA
  • Scheduler

Current Issues

  • Random Number Generators
  • Scheduler
  • SVN Repository for Development
  • Quickbook for Documentations
  • Build System => Move to CMake?
  • Provenance
  • Database


  • Parameter classes will be rewritten by Lukas Gamper and Synge Todo, Emanuel Gull
    • use runtime polymorphism to access the value/convert
    • hierarchical naming
    • make sure that it is written out in the same order as it was read
  • Parallel random number generators: Jeongnim Kim, Matthias Troyer and Brigitte Surer
    • improve seeding for Well generator
    • make a parallel MT class
  • Build system: Synge Todo, Matthias Troyer, Jeongnim Kim, Peter Anders
    • Use NCSA CMake tools for ALPS 2, make it build using CMake
    • Contact Boost.CMake team regarding
      • Quickbook toolchain
      • Building Boost
      • Variants
  • rewrite Alea: Matthias Troyer, Emanuel Gull, Peter Anders
    • record numbers in a class based on Boost.Accumulator
    • write specific evaluation classes, that might later be moved to Boost
  • rewrite Lattice: Sergei Isakov, Matthias Troyer, Lukas Gamper
    • define concepts for building and accessing hypergraphs
    • then implement it
    • afterwards define the lattice descriptions in XML
  • scheduler: Synge Todo, Emanuel Gull, Matthias Troyer
    • flexible tree structure with abstract nodes and a common factory
      • nodes get a process group
    • expose it to the user at various levels in C++ and Python
    • use threading to respond to messages

November 2nd

November 3rd, Morning

Brainstorming on Monte Carlo file format

  • what do we want to store or calculate
    • timeseries (~10^2...10^7 samples), mean, error, autocorrelation (by binning or fitting), equilibration
    • typical sample size: 1...10^8
    • we want to filter, bin, or block the time series (size is determined by covariance matrices)
    • functions (moments, etc) with bootstraping/jackknife (O(10^2)...?)
    • running means of time series
    • histogram of time series
    • mean vs cutoff
    • "movies"
    • Fourier transform (FFT)
    • functions on multiple timeseries (arithmetic operations such as <m^2>-<m>^2)
    • reweighting (store whole timeseries or microcanonical averages)
    • checking reweighting range (from histogram)
    • linear least-squares fitting on (un)correlated data
    • nonlinear least-squares fitting (eg. L^(-x) (1+a L^(-y)))
    • MaxEnt for analytic continuations
  • complex observables
    • extracting real/imaginary part of timeseries
    • Do we need to store errors (i.e. covariance matrices) for complex observables? Currently, no.
  • microcanonical averages for multi-canonical sampling
    • store full timeseries or histograms
  • optimizer
  • random systems?