#+TITLE: The TREXIO library
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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
~.~ 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]__~, 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]__~.
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.