What is Lacaml?
This OCaml-library interfaces two widely used
This allows developers to write high-performance numerical code for
applications that require linear algebra.
The BLAS- and LAPACK-libraries have evolved over about two decades of
time and are therefore extremely mature both in terms of stability
Lacaml interfaces most of the functions in BLAS and LAPACK (many
hundreds!). It supports among other things linear equations, least
squares problems, eigenvalue problems, singular value decomposition
(SVD), Cholesky and QR-factorization, etc.
Many convenience functions for creating and manipulating matrices.
Powerful printing functions for large vectors and matrices and supplemental
information (e.g. row and column headers). Users can specify easily how
much context to print. For example, it is usually sufficient to print
small blocks of the four corners of a large result matrix to manually
verify the correctness of an algorithm. Lacaml uses this approach to
limit the output to human-manageable size.
Integration into the OCaml-toplevel allows for easy experimentation for
students and researchers as well as demonstration for lecturers.
Values of vector and matrix type will be printed automatically without
cluttering the screen.
The OCaml-interface was designed in a way to combine both the possibility
of gaining optimum efficiency (e.g. by allowing the creation of work arrays
outside of loops) with simplicity (thanks to labels and default arguments).
The code is precision-independent and supports both real and complex
transforms in a consistent way. There are four modules that implement
the same interface modulo the precision type and specialized real/complex
functions. If you refer to elements in this interface only, your code
becomes precision- and (if meaningful) real/complex independent, too:
you can choose at anytime whether you want to use single-precision or
double-precision simply by referring to the required module.
You can fully exploit the library within multithreaded programs. Many
numerical routines are likely to run for a long time, but they will never
block other threads. This also means that you can execute several routines
at the same time on several processors if you use POSIX-threads in OCaml.
To make things easy for developers used to the "real" implementation
in FORTRAN but also for beginners who need detailed documentation, both
function- and argument names have been kept compatible to the ones used
in the BLAS- and LAPACK-documentation. Only exception: you need not
prefix functions with
zto indicate the precision
and type of numbers, because the OCaml module system provides us with
a more convenient means of choosing them.
(Almost) all errors are handled within OCaml. Typical mistakes like
passing non-conforming matrices, parameters that are out of range, etc.,
will be caught before calling Fortran code and will raise exceptions.
These exceptions will explain the error in detail, for example the
received illegal parameter and the range of expected legal values.
You can make use of this library by referring to the corresponding module
for the required precision and number type. E.g.:
open Lacaml.S (* Single-precision real numbers *) open Lacaml.D (* Double-precision real numbers *) open Lacaml.C (* Single-precision complex numbers *) open Lacaml.Z (* Double-precision complex numbers *)
These modules become available if you link the
lacaml-library with your
application. The widely used OCaml-tool
findlib will take care of linking
lacaml correctly. If you do not use this tool, you will also have to link
bigarray-library provided by the OCaml-distribution.
Lacaml.?-modules implement the BLAS/LAPACK-interface. Their
Mat provide for vector and matrix
operations that relate to the given precision and number type.
Most functions were implemented using optional arguments (= default arguments).
If you do not provide them at the call-site, sane defaults will be used
instead. Here is an example of a function call:
let rank = gelss in_mat out_mat in (* ... *)
This example computes the solution to a general least squares problem (=
linear regression) using the SVD-algorithm with
in_mat as the matrix
containing the predictor variables and
out_mat as the matrix containing
(possibly many) response variables (this function can handle several response
variables at once). The result is the rank of the matrix. The matrices
provided in the arguments will be overwritten with further results (here:
the singular vectors and the solution matrix).
If the above happened in a loop, this would be slightly inefficient, because
a work-array would have to be allocated (and later deallocated) at each call.
You can hoist the creation of this work array out of the loop, e.g. (
nrhs are problem dependent parameters):
let work = gelss_min_work ~m ~n ~nrhs in for i = 1 to 1000 do (* ... *) let rank = gelss in_mat ~work out_mat in (* ... *) done
All matrices can be accessed in a restricted way, i.e. you can specify
submatrices for all matrix parameters. For example, if some matrix is called
a in the interface documentation, you can specify the left upper corner of
the wanted submatrix for the operation by setting
ar for the row and
for the column (1 by default). A vector
y would have an extra optional
ofsy (also 1 by default). Parameters like
specify the numbers of rows or columns.
Printing vectors and matrices
Here is a toplevel example of printing a large random matrix:
# #require "lacaml";; # open Lacaml.D;; # let mat = Mat.random 100 200;; val mat : Lacaml.D.mat = C1 C2 C3 C198 C199 C200 R1 -0.314362 -0.530711 0.309887 ... 0.519965 -0.230156 0.0479154 R2 0.835658 0.581404 0.161607 ... -0.749358 -0.630019 -0.858998 R3 -0.403421 0.458116 -0.497516 ... 0.210811 0.422094 0.589661 ... ... ... ... ... ... ... R98 -0.352474 0.878897 0.357842 ... 0.150786 -0.74011 0.353253 R99 0.104805 0.984924 -0.319127 ... -0.143679 -0.858269 0.859059 R100 0.419968 0.333358 0.237761 ... -0.483535 -0.0224016 0.513944
Only the corner sections of the matrix, which would otherwise be too large
to display readably, are being printed, and ellipses (
...) are used in
place of the removed parts of the matrix.
If the user required even less context, the
which is also available in each precision module for convenience (here:
Lacaml.D), could be used to indicate how much. In the following example
only two-by-two blocks are requested in each corner of the matrix:
# lsc 2;; - : unit = () # mat;; - : Lacaml.D.mat = C1 C2 C199 C200 R1 -0.314362 -0.530711 ... -0.230156 0.0479154 R2 0.835658 0.581404 ... -0.630019 -0.858998 ... ... ... ... ... R99 0.104805 0.984924 ... -0.858269 0.859059 R100 0.419968 0.333358 ... -0.0224016 0.513944
Applications can use the standard
Format-module in the OCaml-distribution
together with Lacaml printing functions to output vectors and matrices.
Here is an example using labels and showing the high customizability of the
open Lacaml.D open Lacaml.Io let () = let rows, cols = 200, 100 in let a = Mat.random rows cols in Format.printf "@[<2>This is an indented random matrix:@\n@\n%a@]@." (Lacaml.Io.pp_lfmat ~row_labels: (Array.init rows (fun i -> Printf.sprintf "Row %d" (i + 1))) ~col_labels: (Array.init cols (fun i -> Printf.sprintf "Col %d" (i + 1))) ~vertical_context:(Some (Context.create 2)) ~horizontal_context:(Some (Context.create 3)) ~ellipsis:"*" ~print_right:false ~print_foot:false ()) a
The above code might print:
This is an indented random matrix: Col 1 Col 2 Col 3 Col 98 Col 99 Col 100 Row 1 0.852078 -0.316723 0.195646 * 0.513697 0.656419 0.545189 Row 2 -0.606197 0.411059 0.158064 * -0.368989 0.2174 0.9001 * * * * * * * Row 199 -0.684374 -0.939027 0.000699582 * 0.117598 -0.285587 -0.654935 Row 200 0.929341 -0.823264 0.895798 * 0.198334 0.725029 -0.621723
Many other options, e.g. for different padding, printing numbers in
other formats or with different precision, etc., are available for output
Though Lacaml is quite thorough in checking arguments for consistency with
BLAS/LAPACK, an exception to the above is illegal contents of vectors and
matrices. This can happen, for example, when freshly allocated matrices
are used without initialization. Some LAPACK-algorithms may not be able
to deal with floats that correspond to NaNs, infinities, or are subnormal.
Checking matrices on every call would seem excessive. Some functions also
expect matrices with certain properties, e.g. positive-definiteness, which
would be way too costly to verify beforehand.
Degenerate value shapes, e.g. empty matrices and vectors, and zero-sized
operations may also be handled inconsistently by BLAS/LAPACK itself. It is
rather difficult to detect all such corner cases and to predetermine for
all on how they should be handled to provide a sane workaround.
Users are well-advised to to ensure the sanity of the contents of values
passed to Lacaml functions and to avoid calling Lacaml with values having
degenerate dimensions. User code should either raise exceptions if values
seem degenerate or handle unusual corner cases explicitly.
Other sources of usage information
Besides the Lacaml interface file, the API documentation can also be found
BLAS/LAPACK man pages
BLAS and LAPACK binary packages for Unix operating systems usually come
with appropriate man-pages. E.g. to quickly look up how to factorize a
positive-definite, complex, single precision matrix, you might enter:
The corresponding function in Lacaml would be
Lacaml.C.potrf. The naming
conventions and additional documentation for BLAS and LAPACK can be found
at their respective websites.
examples-directory contains several demonstrations of how to use this
library for various linear algebra problems.
It is highly recommended that users install a variant of BLAS (or even
LAPACK) that has been optimized for their system. Processor vendors
(e.g. Intel) usually sell the most optimized implementations for their
CPU-architectures. Some computer and OS-vendors like Apple distribute their
own implementations with their products, e.g.
vecLib, which is part of
There is also ATLAS, a very efficient and
compatible substitute for BLAS. It specializes code for the architecture it
is compiled on. Binary packages (e.g. RPMs) for Linux should be available
from your distribution vendor's site (you must recompile the package to
make sure it is suited to your distribution, see the package documentation
for more details.).
Another alternative for BLAS is OpenBLAS.
If a non-standard library or library location is required, the user can override
the platform-dependent default by setting the following environment variables:
The first one can be used to add compilation flags, and the second one to
override the default linking flags (
Lacaml already passes
-O3 -march=native -ffast-math as compiler flags to fully
exploit SIMD instructions when supported by the used platform. The current
Lacaml code base is probably safe with these options.
Contact Information and Contributing
Please submit bugs reports, feature requests, contributions and similar to
the GitHub issue tracker.
Up-to-date information is available at: https://mmottl.github.io/lacaml
Egbert Ammicht <email@example.com>
Patrick Cousot <Patrick.Cousot@ens.fr>
Sam Ehrlichman <firstname.lastname@example.org>
Florent Hoareau <email@example.com>
Markus Mottl <firstname.lastname@example.org>
Liam Stewart <email@example.com>
Christophe Troestler <Christophe.Troestler@umons.ac.be>
Oleg Trott <firstname.lastname@example.org>
Martin Willensdorfer <email@example.com>