qmckl/org/qmckl.org

#+TITLE: Introduction
#+PROPERTY: comments org
#+SETUPFILE: ../tools/theme.setup
# -*- mode: org -*-

* Using QMCkl

The =qmckl.h= header file installed in the =${prefix}/include= directory
has to be included in C codes when QMCkl functions are used:

#+begin_src c :tangle no
#include "qmckl.h"
#+end_src

In Fortran programs, the =qmckl_f.f90= installed in
=${prefix}/share/qmckl/fortran= interface file should be copied in the source
code using the library, and the Fortran codes should use the ~qmckl~ module as

#+begin_src f90 :tangle no
use qmckl
#+end_src

Both files are located in the =include/= directory.

* Developing in QMCkl

** Literate programming

   In a traditional source code, most of the lines of source files of a program
   are code, scripts, Makefiles, and only a few lines are comments explaining
   parts of the code that are non-trivial to understand. The documentation of
   the prorgam is usually written in a separate directory, and is often outdated
   compared to the code.

   Literate programming is a different approach to programming,
   where the program is considered as a publishable-quality document. Most of
   the lines of the source files are text, mathematical formulas, tables,
   figures, /etc/, and the lines of code are just the translation in a computer
   language of the ideas and algorithms expressed in the text. More importantly,
   the "document" is structured like a text document with sections, subsections,
   a bibliography, a table of contents /etc/, and the place where pieces of code
   appear are the places where they should belong for the reader to understand
   the logic of the program, not the places where the compiler expects to find
   them. Both the publishable-quality document and the binary executable are
   produced from the same source files.

   Literate programming is particularly well adapted in this context, as the
   central part of this project is the documentation of an API. The
   implementation of the algorithms is just an expression of the algorithms in a
   language that can be compiled, so that the correctness of the algorithms can
   be tested.

   We have chosen to write the source files in [[https://karl-voit.at/2017/09/23/orgmode-as-markup-only/][org-mode]] format,
   as any text editor can be used to edit org-mode files. To
   produce the documentation, there exists multiple possibilities to convert
   org-mode files into different formats such as HTML or PDF. The source code is
   easily extracted from the org-mode files invoking the Emacs text editor from
   the command-line in the =Makefile=, and then the produced files are compiled.
   Moreover, within the Emacs text editor the source code blocks can be executed
   interactively, in the same spirit as Jupyter notebooks.

** Source code editing

   For a tutorial on literate programming with org-mode, follow [[http://www.howardism.org/Technical/Emacs/literate-programming-tutorial.html][this link]].

   Any text  editor can be used  to edit org-mode files.  For a better
   user experience Emacs  is recommended.  For users  hating Emacs, it
   is good to  know that Emacs can behave like  Vim when switched into
   ``Evil''  mode.

   In the =tools/init.el= file, we provide a minimal Emacs configuration
   file for vim users. This file should be copied into =.emacs.d/init.el=.

   For users  with a preference  for Jupyter notebooks, we also provide the =tools/nb_to_org.sh= script can convert jupyter notebooks into org-mode
   files.

   Note that pandoc can be used to convert multiple markdown formats into
   org-mode.

** Choice of the programming language

    Most of the codes of the [[https://trex-coe.eu][TREX CoE]] are written in Fortran with some scripts in
    Bash and Python. Outside of the CoE, Fortran is also important (Casino, Amolqc),
    and other important languages used by the community are C and C++ (QMCPack,
    QWalk), and Julia is gaining in popularity. The library we design should be
    compatible with all of these languages.  The QMCkl API has to be compatible
    with the C language since libraries with a C-compatible API can be used in
    every other language.

    High-performance versions of the QMCkl, with the same API, will be rewritten by
    the experts in HPC. These optimized libraries will be tuned for specific
    architectures, among which we can cite x86 based processors, and GPU
    accelerators.  Nowadays, the most efficient software tools to take advantage of
    low-level features of the processor (intrinsics) and of GPUs are for C++
    developers. It is highly probable that the optimized implementations will be
    written in C++, and this is agreement with our choice to make the API
    C-compatible.

    Fortran is one of the most common languages used by the community, and is simple
    enough to make the algorithms readable both by experts in QMC, and experts in
    HPC. Hence we propose in this pedagogical implementation of QMCkl to use Fortran
    to express the QMC algorithms. As the main languages of the library is C, this
    implies that the exposed C functions call the Fortran routine.  However, for
    internal functions related to system programming, the C language is more natural
    than Fortran.

    The Fortran source files should provide a C interface using the ~iso_c_binding~ module. The name of the Fortran source files should end with =_f.f90= to be properly handled by the =Makefile=.  The names of the functions
    defined in Fortran should be the same as those exposed in the API suffixed by =_f=.

    For more guidelines on using Fortran to generate a C interface, see
    [[http://fortranwiki.org/fortran/show/Generating+C+Interfaces][this link]].

** Coding rules

   The authors should follow the recommendations of the C99
   [[https://wiki.sei.cmu.edu/confluence/display/c/SEI+CERT+C+Coding+Standard][SEI+CERT C Coding Standard]].

   Compliance can be checked with =cppcheck= as:

   #+begin_src bash
cppcheck --addon=cert --enable=all *.c &> cppcheck.out
   #+end_src

** Design of the library

   The proposed API should allow the library to: deal with memory transfers
   between CPU and accelerators, and to use different levels of floating-point
   precision.  We chose a multi-layered design with low-level and high-level
   functions (see below).

** Naming conventions

   To avoid namespace collisions, we use =qmckl_= as a prefix for all exported
   functions and variables.  All exported header files should have a file name
   prefixed with =qmckl_=.

   If the  name of the  org-mode file is =xxx.org=, the name  of the
   produced C files should be =xxx.c= and =xxx.h= and the name of the
   produced Fortran file should be =xxx.f90=.

   Arrays are in uppercase and scalars are in lowercase.

   In the  names of  the variables and  functions, only  the singular
   form is allowed.

** Application programming interface

   In the C language, the number of bits used by the integer types can change
   from one architecture to another one. To circumvent this problem, we choose to
   use the integer types defined in ~<stdint.h>~ where the number of bits used for
   the integers are fixed.

   To ensure that the library will be easily usable in /any/ other language
   than C, we restrict the data types in the interfaces to the following:
   - 32-bit and 64-bit integers, scalars and and arrays (~int32_t~ and ~int64_t~)
   - 32-bit and 64-bit floats, scalars and and arrays (~float~ and ~double~)
   - Pointers are always casted into 64-bit integers, even on legacy 32-bit architectures
   - ASCII strings are represented as a pointers to character arrays
     and terminated by a ~'\0'~ character (C convention).
   - Complex numbers can be represented by an array of 2 floats.
   - Boolean variables are stored as integers, ~1~ for ~true~ and ~0~ for ~false~
   - Floating point variables should be by default ~double~ unless explicitly mentioned
   - integers used for counting should always be ~int64_t~

   To facilitate the  use in other languages than C, we will provide some
   bindings in other languages in other repositories.

   # TODO : Link to repositories for bindings
   # To facilitate the  use in other languages than C,  we provide some
   # bindings in other languages in other repositories.

** Global state

   Global variables should  be avoided in the library,  because it is
   possible that one  single program needs to  use multiple instances
   of the library. To solve this  problem we propose to use a pointer
   to  a   [[./qmckl_context.html][=context=]] variable,  built   by  the  library   with  the =qmckl_context_create= function. The <<<=context=>>> contains the global
   state of  the library, and is  used as the first  argument of many
   QMCkl functions.

   The internal structure of the context  is not specified, to give a
   maximum of  freedom to  the different  implementations.  Modifying
   the  state   is  done   by  setters   and  getters,   prefixed  by =qmckl_set_=  an =qmckl_get_=.

** Headers

   A single =qmckl.h= header to be distributed by the library
   is built by concatenating some of the produced header files.
   To facilitate building the =qmckl.h= file, we separate types from
   function declarations in headers. Types should be defined in header
   files suffixed by =_type.h=, and functions in files suffixed by =_func.h=.
   As these files will be concatenated in a single file, they should
   not be guarded by ~#ifndef *_H~, and they should not include other
   produced headers.

   Some particular types that are not exported need to be known by the
   context, and there are some functions to update instances of these
   types contained inside the context. For example, a ~qmckl_numprec_struct~ is present in the context, and the function ~qmckl_set_numprec_range~ takes a context as a parameter, and set a
   value in the ~qmckl_numprec_struct~ contained in the context.
   Because of these intricate dependencies, a private header is
   created, containing the ~qmckl_numprec_struct~. This header is
   included in the private header which defines the type of the
   context. Headers for private types are suffixed by =_private_type.h=
   and headers for private functions, =_private_func.h=.
   Fortran interfaces should also be written in the =*_f_func.f90= file,
   and the types definitions should be written in the =*_f_type.f90= file.

   | File               | Scope   | Description                  |
   |--------------------+---------+------------------------------|
   | =*_type.h=         | Public  | Type definitions             |
   | =*_func.h=         | Public  | Function definitions         |
   | =*_private_type.h= | Private | Type definitions             |
   | =*_private_func.h= | Private | Function definitions         |
   | =*fh_type.f90=     | Public  | Fortran type definitions     |
   | =*fh_func.f90=     | Public  | Fortran function definitions |

** Low-level functions

   Low-level functions are very simple  functions which are leaves of
   the function call tree (they don't call any other QMCkl function).

   These  functions   are /pure/,   and  unaware   of   the   QMCkl =context=. They are not allowed to allocate/deallocate memory, and
   if they need temporary memory it should be provided in input.

** High-level functions

   High-level functions  are at  the top of  the function  call tree.
   They  are  able  to  choose which  lower-level  function  to  call
   depending on the required precision, and do the corresponding type
   conversions.  These functions are  also responsible for allocating
   temporary storage, to simplify the use of accelerators.

   The high-level  functions should be pure,  unless the introduction
   of non-purity is justified. All the side effects should be made in
   the =context= variable.

   # TODO : We need an identifier for impure functions

** Numerical precision

   The number of bits of precision  required for a function should be
   given as an input of low-level computational functions. This input
   will be used to define the values of the different thresholds that
   might be  used to  avoid computing unnecessary  noise.  High-level
   functions  will  use  the  precision specified  in  the =context=
   variable.

** Algorithms

   Reducing the scaling of an  algorithm usually implies also reducing
   its arithmetic  complexity (number  of flops per  byte). Therefore,
   for  small  sizes   \(\mathcal{O}(N^3)\)  and  \(\mathcal{O}(N^2)\)
   algorithms are  better adapted than linear  scaling algorithms.  As
   QMCkl is a  general purpose library, multiple  algorithms should be
   implemented adapted to different problem sizes.