1. Introduction

1.1. Motivations

Complex simulations often combines a number of physics codes, potentially provided by various actors and written in different programming languages. To make them working together, an additional layer, that 'orchestrates' execution of particular codes, and takes care on passing data between 'producers' and 'consumers' is needed. Sometimes the functionality of such layer is provided by dedicated software (aka 'workflow orchestrators', like Kepler https://kepler-project.org/), sometimes it can be handled by much simpler mechanism like Python scripts.  Unfortunately all components ('actors') that constitute a computing scenario ('workflow') must be implemented in the same programming language, defining the same API.

Unfortunately, in most cases, scientific, simulation codes that performs computing intensive calculations  (due to performance reasons) are written in C++ or Fortran, while 'workflow orchestrators' are implemented in (more popular nowadays) languages, like Java, Python, etc. Hence the need for a 'wrapper' that intermediates between native code language and language of the orchestrator. Such wrappers can be developed manually, however users may benefit from a tool that automatise this process - iWrap

1.2. iWrap - actor generator

iWrap is a modular component generator, implemented in Python, used for creating IMAS actors from physics models. This mechanism allows to integrate physics codes written in one language (Fortran, CPP) within complex computing scenarios designed in other language (e.g. Python).

It's plug-in based modular design with clear separation of concerns allows to generate various types of actors and easily change data access paradigm (from dataset descriptor for AL to direct HDC data for instance)

For user conveniency it provides two kinds of interfaces: user friendly graphical interface that allows non-experienced users to define an actor in intuitive way and command line interface foreseen for more advanced users that may want to e.g. automatise actor generation process using scripts.

• iWrap generates a Fortran/CPP wrapper, which intermediates between Kepler actor and user code in terms of:
  • reading/writing of in/out physical data (IDS)
  • passing other arguments to/from the actor
• iWrap creates a Python script (aka an actor) that:
  • calls a user code
  • provides error handling
  • calls debugger (if run in "debug" mode)

2. Preparation of code

A signature of user code must follow strict rules to allow interaction between it and wrapping actor.  Please use following  >>link<< to get detailed guidelines for integration of native code into workflows using iWrap  

3. Code and actor description

iWrap, to properly wrap the code, needs detailed informations about both: the wrapped code and an actor to be generated. A formal description of the code provides information about the programming language used, arguments passed to/from the code, type of these arguments, etc, etc, while an actor description tells iWrap how to name generated actor, where to put it, etc. Such descriptions has to be provided in YAML format file, prepared manually, or automatically with help of iWrap GUI.

3.1.  YAML file syntax

The YAML file consists of two independent parts (aka 'YAML documents'), marked by tags corresponding to their roles: actor_description and code_description. Only code description part is mandatory, and  actor description could be provided in a file, or using iWrap commandline switches or interacting with GUI.

The structure of the file is following:

# actor description part - optional 
--- !actor_description
	<see chapter below for details>
...

# code description part - mandatory
 --- !code_description  
 	<see chapter below for details> 
...
 

3.2. Native code description

Description of the native code has to be provided as a YAML document. It consist of two parts. The first one contains generic information common for all languages, The latter one contains information specific for a given language of the native code (currently defined only for Fortran and CPP). 

3.2.1. Common part

 Generic information common for all (or at least majority of) programming languages.

• programming_language 
  
  • meaning:  language of physics code
  • value: one of predefined values: 'Fortran', 'CPP'
  • example: 'Fortran'
• code_name 
  
  • meaning:
    • name of user method / subroutine to be called,
    • must be exactly the same as name of called method / subroutine
    • it is used also as an actor name
  • value: string
  • example: 'my_subroutine' 
• data_type 
  
  • meaning: data type handled by the physics code
  • value: 'legacy' (currently only 'Legacy IDS' type has been implemented)
  • example: 'legacy'
• arguments  - list of arguments
  • argument definition: 
    • name:
      • meaning: user defined argument name
      • value: string
      • example: equilibrium00   
    • type:
      • meaning: a type of an IDS argument
      • value: 
        • predefined name of one of the IDSes
      • example: 'equilibrium' 
    • intent
      • meaning: determines if given argument is input or output one
      • value: predefined - string "IN", "OUT"
• code_path: 
  • meaning: path to system library (C, CPP) , script (Python), etc containing the physics code, including method/subroutine to be run
  • value: string, valid path to file
  • example: 'any text'  
• code_parameters  - a structure containing parameters and schema entry:
  • parameters :
    • meaning: path to XML file containing user defined parameters of the physics code
    • value: string, valid path to file
    • example: './code_parameters/parameters.xml'
  • schema :
    • meaning: path to XSD file contains schema of XML parameters, to be able to validate them
    • value: string, valid path to file
    • example: './code_parameters/parameters.xsd'

• documentation :
  • meaning: human readable description of native code
  • value: string
  • example: 'any text'

3.2.2. Language specific part - Fortran/C++

• compiler :
  • meaning: the name/vendor of the compiler (and not compiler command!) used to compile native codes
  • value: string, one of vendors of compilers, currently: 'Intel' or 'GCC'
  • example: 'Intel'
• mpi_flavour 
  
  • meaning: MPI compiler flavour to be used
  • values: string, one of:  MPICH, MPICH2, MVAPICH2, OpenMPI, etc.
  • example 'MPICH2'
• open_mp :
  • meaning: if user code should be compiled with OpenMP flag
  • values: boolean
  • example 'true'
• system_libraries :
  • meaning: a list of system libraries, managed using pkg-config mechanism,  that has to be used while native code linking
  • value: a list of system libraries names, as they are published by pkg-config 

  • example: 

    - fftw3f - glib - mkl

• custom_libraries :
  • meaning: additional libraries, not managed by pkg-config mechanism, necessary to link of the physics code :
  • value:  a list of paths to libraries 

  • example: 

    - ./lib/custom/libcustom1.a - ./lib/custom/libcustom2.a

3.2.3. Example - Fortran code description

--- !code_description
programming_language: Fortran
code_name: demo_code
data_type: legacy
arguments:
-   name: equilibrium00
    type: equilibrium
    intent: IN
-   name: equilibrium11
    type: equilibrium
    intent: OUT
code_path: ./lib/libmy_lib.a
code_parameters:
    parameters: ./code_paramneters/parameters.xml
    schema: ./code_paramneters/parameters.xsd
documentation: 'Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do
    eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim
    veniam... '
language_specific:
    compiler: Intel
    mpi_flavour: MPICH2
    open_mp: false
    system_libraries:
    - fftw3f
    - glib
    - mkl
    custom_libraries:
    - ./lib/custom/libcustom1.a
    - ./lib/custom/libcustom2.a

3.3. Actor description

3.3.1. Actor description syntax

• actor_name :
  • meaning: the arbitrary, user defined name of the actor. It determines: the name of class to be generated and directory where actor will be put
  • value: string
  • example: 'core2dist'
• actor_type:
  • meaning:  
  • values: 'python' (currently only python type has been implemented)
  • example 
• data_type:
  • meaning: data type handled at the workflow level 
  • value: 'legacy' (currently only 'Legacy IDS' type has been implemented)
  • example: 'legacy'
• install_dir:
  • optional parameter
  • meaning:  user chosen folder, where an actor will be installed
  • values: string, 
  • example : /my/working/dir/IWRAP_ACTORS
  • if not defined, a default installation directory will be used

3.3.2. Example

--- !actor_description
actor_name: core2dist
actor_type: python
data_type: legacy
install_dir:  /my/working/dir/IWRAP_ACTORS
...

4. Actor generation

4.1. iWrap commandline

4.2.  iWrap graphical interface

>>here<<

5. Usage of actor within workflow

5.1. Importing actor to workflow script

To make an actor class visible inside a workflow script it has to be imported:

from <actor_package>.actor import <actor_class> 

In a current version both: <actor_package> and <actor_class>  are set to the same value provided by user as an 'actor name'.

To import an actor named e.g. 'physics_ii' a correct import will look like:

from physics_ii.actor import physics_ii 

5.2. Actor runtime settings

Among the actor properties one is especially important: runtime_settings.  This property tells the wrapper how native code should be run and defines:

• Run mode
  • Defined by setting: <actor name>.runtime_settings.run_mode = value

  • Import of enumerated values: from <actor name>.python_common.job_settings import RunMode 

  • RunMode.NORMAL (default) - native code is called directly from Python, within the same process (and environment) that workflow script. Usually system resources, shared with other Python threads are limited, however this mode is suitable for most of the actors.   
  • RunMode.STANDALONE - an actor runs native code as executable in a separate system process, having its own environment and (usually) bigger system resources available. This mode is suitable e.g. for memory demanding code.

  • Example: 

    from physics_ii.python_common.job_settings import RunMode
    
    self.physics_ii.runtime_settings.run_mode = RunMode.STANDALONE

• Debug mode:
  • Defined by setting: <actor name>.runtime_settings.debug_mode = value
  • Import of enumerated values: from <actor name>.python_common.job_settings import DebugMode 
  • RunMode.NORMAL (default) - native code is called directly from Python, within the same process (and environment) that workflow script. Usually system resources, shared with other Python threads are limited, however this mode is suitable for most of the actors.   
  • RunMode.STANDALONE - an actor runs native code as executable in a separate system process, having its own environment and (usually) bigger system resources available. This mode is suitable e.g. for memory demanding code.

  • Example: 

    from physics_ii.python_common.job_settings import DebugMode
    
    self.physics_ii.runtime_settings.run_mode = DebugMode.STANDALONE

from core2dist.python_common.job_settings import RunMode, DebugMode

self.actor_cp2ds.runtime_settings.run_mode = RunMode.STANDALONE #self.actor_cp2ds.runtime_settings.debug_mode = DebugMode.ATTACH

5.3. Actor life cycle

5.4.  Workflow example

import sys
import imas,os

from core2dist.actor import core2dist
from core2dist.python_common.job_settings import RunMode, DebugMode

class ExampleWorkflowManager:

    def __init__(self):
        self.actor_cp2ds = core2dist()
        self.input_entry = None
        self.output_entry = None

    def init_workflow(self):

        # INPUT/OUTPUT CONFIGURATION
        shot                = 134174
        run_in              = 37
        input_user_or_path  = 'public'
        input_database      = 'iter'
        run_out             = 10
        output_user_or_path = os.getenv('USER')
        output_database     = input_database

        # OPEN INPUT DATAFILE TO GET DATA FROM IMAS SCENARIO DATABASE
        print('=> Open input datafile')
        self.input_entry = imas.DBEntry(imas.imasdef.MDSPLUS_BACKEND,input_database,shot,run_in,input_user_or_path)
        self.input_entry.open()
        
        # CREATE OUTPUT DATAFILE
        print('=> Create output datafile')
        self.output_entry = imas.DBEntry(imas.imasdef.MDSPLUS_BACKEND,output_database,shot,run_out,output_user_or_path)
        self.output_entry.create()

        # # # # # # # # Initialization of ALL actors  # # # # # # # #
        #
        actor_run_mode = os.getenv( 'ACTOR_RUN_MODE', 'NORMAL')
        if actor_run_mode == 'STANDALONE':
            print('Running STANDALONE version.')
            self.actor_cp2ds.runtime_settings.run_mode = RunMode.STANDALONE
        #self.actor_cp2ds.runtime_settings.debug_mode = DebugMode.ATTACH
        self.actor_cp2ds.initialize()
    
    def execute_workflow(self):
        # READ INPUT IDSS FROM LOCAL DATABASE
        print('=> Read input IDSs')
        input_core_profiles = self.input_entry.get('core_profiles')

        # EXECUTE PHYSICS CODE
        print('=> Execute physics code')

        output_distribution_sources = self.actor_cp2ds(input_core_profiles)        
        
        # SAVE IDSS INTO OUTPUT FILE
        print('=> Export output IDSs to local database')
        self.output_entry.put(output_distribution_sources)
        print('Done exporting.')

    def end_workflow(self):
        
        # Finalize ALL actors 
        self.actor_cp2ds.finalize()

        #other finalizastion actions
        self.input_entry.close()
        self.output_entry.close()

manager = ExampleWorkflowManager()

manager.init_workflow()
manager.execute_workflow()
manager.end_workflow()