DARTHs cheat sheet


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DARTHs are composable infrastructures whose parts are loosely connected, communicate with standard protocols and can easily be substituted. There is not such a thing like a DARTH but a DARTH solutions whose parts can be disassembled and recomposed in new solutions, exactly it happens for Linux distributions (distros).  Distros need usually an integrator  of resources which is a further component of DARTHs.

  • Must be Open
  • Provided on demand over the cloud
  • Discoverable on the web
  • Provided in standardized self-explanatory formats
  • Retrievable on the base on open APIs on the most common computer languages
DARTHs IT Architecture requirements
DARTHs serve different users and roles
Therefore they are agnostic:
  •  with respect to the science
  • the programming language
  • The operative system
  • Modelling styles and paradigm
  • They are lightweight with respect to programming habits, meaning they should be minimalist in adding programming rules and aims to maintain the code short
  • Variables names should be mapped into standard names that provide an unique identification
in Open DARTHs,
  • Programs must be open source and developed with open tools
  • Simulations and operations must be traceable and replicable by construction
  • Chunks of data, codes and modelling solutions should organized in standard way and be deployable over the web for third parties testing snd reuse
Hydrological models for DARTHs
  •  need to be able to easy any flavor of modelling effort through ODEs (systems of ordinary differential equations), PDEs (system of partial differential equations), MS (Statistical Modelling), ML (Machine Learning)
  • should be deployed in model components (A component model is a model that is used to encapsulate equations or specific tasks into a reusable form. Components can be connected at runtime and communicate exchanging data in RAM memory)
  • need to be deployable along the web on all the available platforms.
  • model-to-model (components) communication should be allowed through API
DARTHs programming practices
DARTHs must
  • Adopt software quality testing for any component separately
  • Adopting good programming practices
  • Promote literate computing (and literate programming)
DARTHs and Computing facilities
  • They use various form  of parallelism but overall, parallelism should be provided as a service without having to change programming habits of models developers.
  • Workflows of tasks that can be schematize on graphs should be internally parallelized using piping methods.
  • Models on grids should use middleware that separates the parallelization from the “physics” of implementation and should be as less invasive as possible.
  • They can use multicores, multiprocessor, high end computers, distributed computing without necessarily direct intervention of the programmer
  • They should use the natural partition of Earth surface in river catchments and subcatchments to split the computational work.
  • The complex retrieval chain has to become explicit and adaptable to the modeling
  • Data Assimilation must become part of the modelling chain
DARTHs and Science Reliability and practices
  • The quantification of uncertainty must become  inherently part of an open DARTH. Without it the quantitative indication lose their  credibility.
  • DARTHs must support open science, practices without which, science becomes a nonsense.

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