trexio/lib.org

#+TITLE: The TREXIO library
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* Format specification

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  The TREXIO format is designed to store all the necessary information
  to represent a wave function.
  One notable feature of TREXIO is that it is self-contained, meaning
  that all the parameters needed to recreate the wave function are
  explicitly stored within the file, eliminating the need for external
  databases. For example, instead of storing the name of a basis set
  (such as cc-pVDZ), the actual basis set parameters used in the
  calculation are stored.

** Organization of the data

   The data in TREXIO are organized into *groups*, each containing
   multiple *attributes* defined by their *type* and *dimensions*.  Each
   attribute within a group corresponds to a single scalar or array
   variable in a code.  In what follows, the notation
 ~<group>.<attribute>~ will be used to identify an attribute within a
   group. For example, ~nucleus.charge~ refers to the
 ~charge~ attribute in the ~nucleus~ group.  It is an array of type
 ~float~ with dimensions ~nucleus.num~, the attribute describing the
   number of nuclei.

** Data types

   So that TREXIO can be used in any language, we use a limited number
   of data types.  The main data types are ~int~ for integers,
 ~float~ for floating-point values, and ~str~ for
   character strings.  For complex numbers, their real and imaginary
   parts are stored as ~float~.  To minimize the risk of integer
   overflow and accuracy loss, numerical data types are stored using
   64-bit representations by default.  However, in specific cases where
   integers are bounded (such as orbital indices in four-index
   integrals), the smallest possible representation is used to reduce the
   file size. The API handles any necessary type conversions.

   There are also two types derived from ~int~: ~dim~ and ~index~.
 ~dim~ is used for dimensioning variables, which are positive integers
   used to specify the dimensions of an array.  In the previous example,
 ~nucleus.num~ is a dimensioning variable that specifies the
   dimensions of the ~nucleus.charge~ array. ~index~ is used for
   integers that correspond to array indices, because some languages
   (such as C or Python) use zero-based indexing, while others (such as
   Fortran) use one-based indexing. For convenience, values of the
 ~index~ type are shifted by one when TREXIO is used in one-based
   languages to be consistent with the semantics of the language.
   You may also encounter some ~dim readonly~ variables.  It means
   that the value is automatically computed and written by the TREXIO
   library, thus it is read-only and cannot be (over)written by the
   user.

   Arrays can be stored in either dense or sparse formats.  If the
   sparse format is selected, the data is stored in coordinate format.
   For example, the element ~A(i,j,k,l)~ is stored as a quadruplet of
   integers $(i,j,k,l)$ along with the corresponding value. Typically,
   two-dimensional arrays are stored as dense arrays, while arrays with
   higher dimensions are stored in sparse format.
   For sparse data structures the data can be too large to fit in memory
   and the data needs to be fetched using multiple function calls to
   perform I/O on buffers.  For more information on how to read/write
   sparse data structures, see the [[./examples.html][examples]].

   For the Configuration Interaction (CI) and Configuration State
   Function (CSF) groups, the ~buffered~ data type is introduced, which
   allows similar incremental I/O as for ~sparse~ data but without the
   need to write indices of the sparse values.

   For determinant lists (integer bit fields), the ~special~ attribute
   is present in the type. This means that the source code is not
   produced by the generator, but hand-written.

   Some data may be complex. In that case, the real part should be stored
   in the variable, and the imaginary part will be stored in the variable
   with the same name suffixed by ~_im~.

* The TREXIO library

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  The TREXIO library is written is the C language, and is licensed under
  the open-source 3-clause BSD license to allow for use in all types of
  quantum chemistry software, whether commercial or not.

  The design of the library is divided into two main sections: the
  front-end and the back-end.  The front-end serves as the interface
  between users and the library, while the back-end acts as the
  interface between the library and the physical storage.

** The front-end

   By using the TREXIO library, users can store and extract data in a
   consistent and organized manner. The library provides a user-friendly
   API, including functions for reading, writing, and checking for the
   existence of data.  The functions follow the pattern
 ~trexio_[has|read|write]_<group>_<attribute>~, where the
   group and attribute specify the particular data being accessed.  It
   also includes an error handling mechanism, in which each function call
   returns an exit code of type ~trexio_exit_code~, explaining
   the type of error.
   This can be used to catch exceptions and improve debugging in the
   upstream user application.

   To ensure the consistency of the data, the attributes can only be
   written if all the other attributes on which they explicitly depend
   have been written.  For example, as the ~nucleus.coord~ array is
   dimensioned by the number of nuclei ~nucleus.num~, the ~nucleus.coord~
   attribute can only be written after ~nucleus.num~.  However, the
   library is not aware of non-explicit dependencies, such as the
   relation between the electron repulsion integrals (ERIs) and MO
   coefficients.  A complete control of the consistency of the data is
   therefore impossible, so the attributes were chosen to be by default
 /immutable/.  By only allowing data to be written only once, the
   risk of modifying data in a way that creates inconsistencies is
   reduced.  For example, if the ERIs have already been written, it would
   be inconsistent to later modify the MO coefficients.  To allow for
   flexibility, the library also allows for the use of an /unsafe/
   mode, in which data can be overwritten.  However, this mode carries
   the risk of producing inconsistent files, and the ~metadata~ group's
 ~unsafe~ attribute is set to ~1~ to indicate that the file has
   potentially been modified in a dangerous way.  This attribute can be
   manually reset to ~0~ if the user is confident that the modifications
   made are safe.

** The back-end

   At present, TREXIO supports two back-ends: one relying only on the
   C standard library to produce plain text files (the so-called /text/
   back-end), and one relying on the HDF5 library.

   With the text back-end, the TREXIO "file" is a directory containing
   multiple text files, one for each group.  This back end is intended
   to be used in development environments, as it gives access to the
   user to the standard tools such as ~diff~ and ~grep~.
   In addition, text files are better adapted than binary files for
   version control systems such as Git, so this format can be also
   used for storing reference data for unit tests.

   HDF5 is a binary file format and library for storing and managing
   large amounts of data in a hierarchical structure.  It allows users
   to manipulate data in a way similar to how files and directories
   are manipulated within the file system.  The HDF5 library provides
   optimal performance through its memory mapping mechanism and
   supports advanced features such as serial and parallel I/O,
   chunking, and compression filters.  However, HDF5 files are in
   binary format, which requires additional tools such as ~h5dump~ to
   view them in a human-readable format.  It is widely used in
   scientific and engineering applications, and is known for its high
   performance and ability to handle large data sets efficiently.

   The TREXIO HDF5 back-end is the recommended choice for production
   environments, as it provides high I/O performance.  Furthermore,
   all data is stored in a single file, making it especially suitable
   for parallel file systems like Lustre.  These file systems are
   optimized for large, sequential I/O operations and are not
   well-suited for small, random I/O operations.  When multiple small
   files are used, the file system may become overwhelmed with
   metadata operations like creating, deleting, or modifying files,
   which can adversely affect performance.

   In a benchmarking program designed to compare the two back-ends of
   the library, the HDF5 back-end was found to be significantly faster
   than the text back-end.  The program wrote a wave function made up
   of 100 million Slater determinants and measured the time taken to
   write the Slater determinants and CI coefficients.  The HDF5
   back-end achieved a speed of $10.4\times10^6$ Slater determinants
   per second and a data transfer rate of 406 MB/s, while the text
   back-end had a speed of $1.1\times10^6$ determinants per second and
   a transfer rate of 69 MB/s.  These results were obtained on a DELL
   960 GB mix-use solid-state drive (SSD). The HDF5 back-end was able
   to achieve a performance level close to the peak performance of the
   SSD, while the text back-end's performance was limited by the speed
   of the CPU for performing binary to ASCII conversions.

   In addition to the HDF5 and text back-ends, it is also possible to
   introduce new back-ends to the library.  For example, a back-end
   could be created to support object storage systems, such as those
   used in cloud-based applications or for archiving in open data
   repositories.

** Supported languages

   One of the main benefits of using C as the interface for a library is
   that it is easy to use from other programming languages.  Many
   programming languages, such as Python or Julia, provide built-in
   support for calling C functions, which means that it is relatively
   straightforward to write a wrapper that allows a library written in C
   to be called from another language.
   In general, libraries with a C interface are the easiest to use from
   other programming languages, because C is widely supported and has a
   simple, stable application binary interface (ABI).  Other languages,
   such as Fortran and C++, may have more complex ABIs and may
   require more work to interface with them.

   TREXIO has been employed in codes developed in various programming
   languages, including C, C++, Fortran, Python, OCaml, and Julia.  While
   Julia is designed to enable the use of C functions without the need
   for additional manual interfacing, the TREXIO C header file was
   automatically integrated into Julia programs using the
 ~CBindings.jl~ package.
   In contrast, specific bindings have been provided for Fortran, Python,
   and OCaml to simplify the user experience.

   In particular, the binding for Fortran is not distributed as multiple
   compiled Fortran module files (~.mod~), but instead as a single
   Fortran source file (~.F90~).  The distribution of the source file
   instead of the compiled module has multiple benefits.  It ensures that
   the TREXIO module is always compiled with the same compiler as the
   client code, avoiding the compatibility problem of ~.mod~ files
   between different compiler versions and vendors.  The single-file
   model requires very little changes in the build system of the user's
   codes, and it facilitates the search for the interface of a particular
   function.  In addition, advanced text editors can parse the TREXIO
   interface to propose interactive auto-completion of the TREXIO
   function names to the developers.

   Finally, the Python module, partly generated with SWIG and fully
   compatible with NumPy, allows Python users to interact with the
   library in a more intuitive and user-friendly way.  Using the Python
   interface is likely the easiest way to begin using TREXIO and
   understanding its features.  In order to help users get started with
   TREXIO and understand its functionality, tutorials in Jupyter
   notebooks are available on GitHub
   (https://github.com/TREX-CoE/trexio-tutorials), and can be executed
   via the Binder platform.


** Source code generation and documentation

   Source code generation is a valuable technique that can significantly
   improve the efficiency and consistency of software development.  By
   using templates to generate code automatically, developers can avoid
   manual coding and reduce the risk of errors or inconsistencies.  This
   approach is particularly useful when a large number of functions
   follow similar patterns, as in the case of the TREXIO library, where
   functions are named according to the pattern
 ~trexio_[has|read|write]_<group>_<attribute>~.
   By generating these functions from the format specification using
   templates, the developers can ensure that the resulting code follows a
   consistent structure and is free from errors or inconsistencies.

   The description of the format is written in a text file in the Org
   format. Org is a structured plain text format, containing information
   expressed in a lightweight markup language similar to the popular
   Markdown language.  While Org was introduced as a mode of the GNU
   Emacs text editor, its basic functionalities have been implemented in
   most text editors such as Vim, Atom or VS Code.

   There are multiple benefits in using the Org format.  The first
   benefit is that the Org syntax is easy to learn and allows for the
   insertion of equations in \LaTeX{} syntax.  Additionally, Org files
   can be easily converted to HyperText Markup Language (HTML) or
   Portable Document Format (PDF) for generating documentation.  The
   second benefit is that GNU Emacs is a programmable text editor and
   code blocks in Org files can be executed interactively, similar to
   Jupyter notebooks.  These code blocks can also manipulate data defined
   in tables and this feature is used to automatically transform tables
   describing groups and attributes in the documentation into a
   JavaScript Object Notation (JSON) file.
   This JSON file is then used by a Python script to generate the needed
   functions in C language, as well as header files and some files
   required for the Fortran, Python, and OCaml interfaces.

   With this approach, contributions to the development of the TREXIO
   library can be made simply by adding new tables to the Org file, which
   can be submitted as /pull requests/ on the project's GitHub
   repository (https://github.com/trex-coe/trexio).  Overall, this
   process allows for a more efficient and consistent development process
   and enables contributions from a wider range of individuals,
   regardless of their programming skills.

** Availability

   The TREXIO library is designed to be portable and easy to install
   on a wide range of systems.  It follows the C99 standard to ensure
   compatibility with older systems, and can be configured with either
   the GNU Autotools or the CMake build systems.  The only external
   dependency is the HDF5 library, which is widely available on HPC
   platforms and as packages on major Linux distributions.  Note that
   it is possible to disable the HDF5 back-end at configuration time,
   allowing TREXIO to operate only with the text back-end and have
   zero external dependencies.  This can be useful for users who may
   not be able to install HDF5 on certain systems.

   TREXIO is distributed as a tarball containing the source code,
   generated code, documentation, and Fortran interface.  It is also
   available as a binary ~.deb~ package for Debian-based Linux
   distributions and as packages for Guix, Spack and Conda. The Python
   module can be found in the PyPI repository, the OCaml binding is
   available in the official OPAM repository, and the ~.deb~ packages
   are available in Ubuntu 23.04.