// doc/matrixwrap.dox
// Copyright 2009-2011 Microsoft Corporation Arnab Ghoshal
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namespace kaldi {
/** \page matrixwrap External matrix libraries
Here we describe how our \ref matrix "matrix library" makes use of
external libraries.
\section matrixwrap_summary Overview
The matrix code in Kaldi is mostly a wrapper on top of the linear-algebra
libraries BLAS and LAPACK. The code has been designed to be as flexible as
possible in terms of what libraries it can use. Currently it supports four
options:
- Intel MKL, which provides both BLAS and LAPACK (the default)
- OpenBLAS, which provides BLAS and LAPACK
- ATLAS, which is an implementation of BLAS plus a subset of LAPACK (with a different interface)
- Some implementation of BLAS plus CLAPACK (note: this has not been tested recently).
The code has to "know" which of these four options is being used, because
although in principle BLAS and LAPACK are standardized, there are some
differences in the interfaces. The Kaldi code requires exactly one of the
three macros \c HAVE_ATLAS, \c HAVE_CLAPACK, \c HAVE_OPENBLAS or \c HAVE_MKL
to be defined (normally using \c -DHAVE_ATLAS as an option to the compiler).
It must then be linked with the appropriate libraries. The code that deals
most directly with including the external libraries and setting up the
appropriate typedef's and defines, is in \ref kaldi-blas.h. However, the rest
of the matrix code is not completely insulated from these issues because the
ATLAS and CLAPACK versions of higher-level routines are called differently (so
we have a lot of "#ifdef HAVE_ATLAS" directives and the like). Additionally,
some routines are not even available in ATLAS so we have had to implement them
ourselves.
The "configure" script in the "src" directory is responsible for setting up
Kaldi to use the libraries. It does this by creating the file "kaldi.mk" in
the "src" directory, which gives appropriate flags to the compiler. If called
with no arguments it will use any Intel MKL installation it can find in
"normal" places in your system, but it is configurable. Run the script with
the \c \--help option for the complete option list.
\section matrixwrap_matalgebra Understanding BLAS and LAPACK
Because we refer a lot to BLAS (and more often CBLAS) and LAPACK (or, rarely,
CLAPACK) in this section, we briefly explain what it is.
\subsection matrixwrap_blas Basic Linear Algebra Subroutines (BLAS)
BLAS is a set of subroutine declarations that correspond to low-level
matrix-vector operations. There is BLAS Level 1 (vector-vector), Level 2
(vector-matrix) and Level 3 (matrix-matrix). They have names like \c daxpy
(for \"double-precision \b a \b x plus \b y\"), and \c dgemm
(for "double-precision general matrix-matrix multiply"). BLAS has various
actual implementations. The reference
implementation of BLAS originated back in 1979, and has been maintained
since by Netlib. The reference implementation lacks any optimization
whatsoever, and exists solely as a touchstone to validate the correctness of
other implementations. MKL, ATLAS and OpenBLAS provide optimized
implementations of BLAS.
CBLAS is just the C language interface to BLAS.
\subsection matrixwrap_lapack Linear Algebra PACKage (LAPACK)
LAPACK is a set of linear-algebra routines, originally written in Fortran. It
includes higher-level routines than BLAS, such as matrix inversion, SVD, etc.
The reference implementation of
LAPACK was implemented and has been maintained by Netlib. LAPACK
internally uses BLAS. It is possible to mix-and-match LAPACK and BLAS
implementations (e.g. Netlib's LAPACK with ATLAS's BLAS).
CLAPACK is a version of LAPACK that has been converted from Fortan to C
automatically using the f2c utility. Because of this, the f2c library is
required during linking with the "original" CLAPACK (usually \c -lg2c or
\c -lf2c).
MKL provides complete C-callable interfaces for its own BLAS and LAPACK
implementations; no additional libraries are required.
\section matrixwrap_mkl Intel Math Kernel Library (MKL)
Intel MKL provides C-language interface to a high-performance implementation
of the BLAS and LAPACK routines, and is currently the preferred CBLAS/CLAPACK
provider for Kaldi. To use MKL with Kaldi use the \c -DHAVE_MKL compiler flag.
Previously MKL used to be a paid product. Starting 2017, Intel made MKL freely
available and allows royalty-freely runtime redistribution even for commercial
application (although, just like, for example, CUDA, it is still a
closed-source commercial product).
MKL provides a very highly optimized implementation of linear algebra
routines, and especially on Intel CPUs. In fact, the library contains multiple
code paths, which are selected at runtime depending on individual features of
the CPU it is being loaded on. Thus with MKL you will automatically benefit
from all features and instruction sets (such as AVX2 and AVX512) if they are
available on your CPU, without any additional configuration. These
instructions accelerate linear algebra operations on CPU significantly. It is
usually a good idea to use a recent MKL version if your CPU is of a newer
architecture.
To simplify MKL setup on Linux, we provide a script
\c tools/extras/install_mkl.sh. We install only 64-bit binaries for MKL, but
once the \c install_mkl.sh script completes successfully once, the Intel
repositories are registered on your system, and you can both obtain new
versions and 32-bit libraries using your system's package manager.
For Mac and Windows,
download the installer from Intel's Web site (registration may be
required). Refer to the same page in case the above Linux script does not
support your Linux distribution. The Intel installers (Mac, Windows) let you
select the 32-bit and 64-bit packages separately. To run Kaldi training
recipes only the 64-bit version is required.
We have tested Kaldi extensively with 64-bit libraries under Linux and
Windows.
The
MKL Link Line Advisor is an interactive Web tool that allows configuring
the compiler flags for various systems and compilers, in case our "configure"
script does not cover it.
\n \b NOTE: Do not use the the multithreaded mode for
Kaldi training (select "sequential" as the threading option). Our script and
binary setups are designed to run multiple processes on a single machine,
presumably maxing out its CPU, and an attempt to multi-thread linear algebra
computations will only adversely impact the performance.
\section matrixwrap_atlas Automatically Tuned Linear Algebra Software (ATLAS)
ATLAS is a well known implementation of BLAS plus a subset of LAPACK. The
general idea of ATLAS is to tune to the particular processor setup, so the
compilation process is quite complex and can take a while. For this reason,
it can be quite tricky to compile ATLAS. On UNIX-based systems, you can't even do it unless you
are root or are friendly with your system administrator, because to compile
it you need to turn off CPU throttling; and on Windows, ATLAS does not compile
"natively", only in Cygwin. Sometimes it can be a better bet to find libraries that
have been compiled by someone else for your particular platform, but we can't offer
much advice on how to do this. ATLAS generally performs better
than the "reference BLAS" available from Netlib. ATLAS only includes
a few LAPACK routines. These include matrix inversion and Cholesky factorization,
but not SVD. For this reason we have implemented a couple more of the LAPACK
routines (SVD and eigenvalue decomposition); see
the next section.
ATLAS conforms to the BLAS interface, but its interface for the subset of
LAPACK routines that it provides is not the same as Netlib's (it's more C-like
and less FORTRAN-ish). For this reason, there are quite a number of \#ifdef's
in our code to switch between the calling styles, depending whether we are
linking with ATLAS or CLAPACK.
\subsection matrixwrap_atlas_install_windows Installing ATLAS (on Windows)
For instructions on how to install ATLAS on Windows (and note that these
instructions require Cygwin), see the file windows/INSTALL.atlas
in our source distribution. Note that our Windows setup is not being
actively maintained at the moment and we don't anticipate that it will work
very cleanly.
\subsection matrixwrap_atlas_install_linux Installing ATLAS (on Linux)
If your system does not have ATLAS installed, or there are no pre-built binaries
available, you will need to install ATLAS from source. Even if your system has
pre-built binaries available, they may not be the best binaries possible for your
architecture so it is probably a better idea to compile from source.
The easiest way to do this
is to cd from "src" to "../tools" and to run ./install_atlas.sh.
If this does not work, the detailed installation
instructions can be found at: http://math-atlas.sourceforge.net/atlas_install/.
One useful note is that before installing ATLAS you should turn off CPU
throttling using "cpufreq-selector -g performance" (cpufreq-selector may be in
sbin), if it is enabled (see the ATLAS install page). You can first try running the
"install_atlas.sh" script before doing this, to see whether it works-- if CPU
throttling is enabled, the ATLAS installation scripts will die with an error.
\section matrixwrap_openblas OpenBLAS
Kaldi now supports linking against the OpenBLAS library, which is an implementation
of BLAS and parts of LAPACK. OpenBLAS also automatically compiles Netlib's implementation of LAPACK,
so that it can export LAPACK in its entirety.
OpenBLAS is a fork of the GotoBLAS project (an assembler-heavy implementation of BLAS) which is no longer being
maintained. In order to use GotoBLAS you can cd from "src" to "../tools", type
"make openblas", then cd to "../src" and give the correct option to the "configure" script
to use OpenBLAS (look at the comments at the top of the configure script to find this option).
Thanks to Sola Aina for suggesting this and helping us to get this to work.
\section matrixwrap_jama Java Matrix Package (JAMA)
JAMA is an implementation of linear-algebra routines for Java, written
in collaboration between NIST and MathWorks and put into the public domain
(see math.nist.gov/javanumerics/jama). We used some of this code to fill
in a couple of holes in ATLAS-- specifically, if we're compiling with
-DHAVE_ATLAS, we don't have the CLAPACK routines for SVD and eigenvalue
decomposition available, so we use code from JAMA that we translated into
C++. See the EigenvalueDecomposition class, and the function MatrixBase::JamaSvd.
The user of the matrix library should never have to interact with this code
directly.
\section matrixwrap_linking_errors Linking errors you might encounter
To make sure the matrix library is compiling correctly, type "make" in the matrix/
directory and see if it succeeds. A lot of compilation issues will manifest themselves
as linking errors. In this section we give a summary of some of the more common
linking errors (at least, those that relate specifically to the matrix library).
Depending on the compilation option (-DHAVE_CLAPACK, -DHAVE_LAPACK or -DHAVE_MKL),
the code will be expecting to link with different things. When debugging linking
errors, bear in mind that the problem could be a mismatch between the compilation
options and the libraries that you actually linked.
\subsection matrix_err_f2c f2c or g2c errors
The f2c library is often required if you link with CLAPACK, because it
was created with f2c and that tool requires you to link with its own library.
Not that with recent versions of gcc you have to link with -lg2c not -lf2c.
The symbols that will be missing if this is the problem, include:
s_cat, pow_dd, r_sign, pow_ri, pow_di, s_copy, s_cmp, d_sign
\subsection matrix_err_clapack CLAPACK linking errors
You will get these errors if you compiled with -DHAVE_CLAPACK but did
not provide the CLAPACK library. The symbols you will be missing are:
sgetrf_, sgetri_, dgesvd_, ssptrf_, ssptri_, dsptrf_, dsptri_, stptri_, dtptri_
This will usually be called something like liblapack.a or if using a
dynamic library, you would
type -llapack. Be careful-- this has the same name as the ATLAS-supplied
library "lapack" (see section \ref matrix_err_clapack),
but it supplies different symbols. The native CLAPACK version of liblapack
has symbols like those above (e.g. sgesvd_, sgetrf_), but the ATLAS version
has symbols like clapack_sgetrf and also ones like ATL_sgetrf.
\subsection matrix_err_blas BLAS linking errors
You will get these errors if you failed to link against an implementation
of BLAS. These errors can also occur if libraries are linked in the wrong
order. CLAPACK requires BLAS, so you have to link BLAS after CLAPACK.
The symbols you will see if you failed to link with BLAS include:
cblas_sger, cblas_saxpy, cblas_dapy, cblas_ddot, cblas_sdot, cblas_sgemm, cblas_dgemm
To fix these, link with a static library like libcblas.a, or do -lcblas (assuming
such a library is on your LD_LIBRARY_PATH). This library may come from ATLAS (which
is preferable), or from Netlib (the "reference BLAS"). To the best of my current
knowledge they have the same interface.
\subsection matrix_err_cblaswrap cblaswrap linking errors
CLAPACK seems to rely on symbols like f2c_sgemm that are some kind of wrapping
of symbols like cblas_sgemm and so on. I'm not sure exactly what is being
wrapped, and why. Anyway, the effect is that you may need to include a library
named libcblaswr.a or dynamically using -lcblaswr, if you are using Netlib's
CLAPACK. The cblaswrap library should be invoked before the cblas one. If you
are missing cblaswrap, you will see errors about symbols like:
f2c_sgemm, f2c_strsm, f2c_sswap, f2c_scopy, f2c_sspmv, f2c_sdot, f2c_sgemv
and so on (there are a lot of these symbols).
\subsection matrix_err_atl_blas Missing the ATLAS implementation of BLAS
If you linked with an ATLAS implementation of BLAS but only did -lcblas (or compiled
with libcblas.a), but did not do -latlas (or compile with libatlas.a), you will have
a problem because ATLAS's BLAS routines like cblas_sger internally call things that are
in libatlas. If you have this problem you will have undefined references like:
ATL_dgemm, ATL_dsyrk, ATL_dsymm, ATL_daxpy, ATL_ddot, ATL_saxpy, ATL_dgemv, ATL_sgemv
\subsection matrix_err_atl_clapack Missing the ATLAS implementation of (parts of) CLAPACK
These errors can only occur if you compiled with the -DHAVE_ATLAS option.
Atlas's name for the CLAPACK routines are different from clapack's own (they
have clapack_ prepended to indicate the origin, which can be quite confusing).
If you have undefined references to the following symbols:
clapack_sgetrf, clapack_sgetri, clapack_dgetrf, clapack_dgetri
then it means you failed to link with an ATLAS library containing these symbols.
This may be variously called liblapack.a, libclapack.a or liblapack_atlas.a,
but you can tell that it is the right one if it defines a symbol called ATL_cgetrf
(type "nm | grep ATL_cgetrf" to see). You may be able to link
dynamically with this library using -llapack or some similar option.
Watch out, because a library called liblapack.a or liblapack.so could
be CLAPACK or it could be ATLAS's version of CLAPACK, and as noted in section
\ref matrix_err_f2c, they supply different symbols. The only way to find
out is to look inside it using "nm" or "strings".
*/
}