2 Getting started
In order to enjoy the full power of Lisplab you
must install some foreign libraries. These are
- BLAS – Basic Linear Algebra Subprograms. Preferably the Atlas build.
- LAPACK – Matrix library. Preferably the Atlas build.
- FFTW – The fastest Fast Fourier Transform available.
Lisplab has been developed with SBCL, SLIME and ASDF on Linux,
and there are yet unnecessary bindings to these platforms.
- Some of the optimized lisp code uses the
- The FFTW FFI works only for SBCL.
- The Matlisp FFI should in theory be portable to other lisps and
Windows, but it has not yet been tested.
*READ-DEFAULT-FLOAT-FORMAT* must be
when compiling Slatec. This is a minor problem.
Except from this, Lisplab should be self-contained and not depend on
any other projects.
Lisplab is ASDF installable, but before you come so far you need to
specify the location of the foreign libraries.
You specify these in three special variables,
that live their lives in the Common-Lisp-User package.
You can either assign them on the top-level, in you Common Lisp
installation file, in
probably the easiest: directly in
- Lisplab –
the full Lisplab installation.
- Lisplab-base –
the part of Lisplab without external dependencies.
- Lisplab-matlisp –
FFIs to BLAS and LAPACK. These are modified version from Matlisp.
- Lisplab-fftw –
FFI to FFTW for Fast Fourier Transform.
- Slatec –
special functions, generated from Fortran by f2cl.
Originally made for Maxima.
- Quadpack –
integration routines, generated from Fortran by f2cl.
The simplest way to test Lisplab is to load
If you have problems loading, first look at
and see if you can hack it. Then look at
To install BLAS, LAPACK, and FFTW, if you are too lazy to do a custom build,
and is lucky enough to administer a Debian or Ubuntu machine,
you typically write
# aptitude install libatlas3gf-base
# aptitude install libfftw3-3
2.4 Naming conventions
- The matrix classes and constructors follow the naming convention
from BLAS where you give names based on element type and
The most used types are f - float, d - double,
c - complex float, z - complex double float,
while for matrix structure ge - general, di - diagonal,
and many more. So matrix-dge is a general matrix
with double float elements, while matrix-zge is a
general matrix with complex double float elements.
- The generic functions of the basic algebra start with a dot:
On numbers these functions work as the non-dotted Common Lisp functions
and on matrices they work elementwise.
- Linear algebra functions tend to start with m:
but this conventions is not strictly followed.
- The naming convention of files follow the layered structure of
Lisplab, with level0 to level3.
- See Structure, for more about the naming conventions
of matrix classes.
2.5 Status - past and future
Lisplab has been developed for physics simulations and data handling.
Lisplab started as a refactoring of Matlisp, but most of the code
has been replaced and a lot new code has been written.
The only Matlisp code that is kept is the interfaces to BLAS and LAPACK.
Some large extensions that could be fun to do:
An of course a lot more linear algebra and matrix stuff.
- Parallel computation, e.g. using MPI.
- More native linear algebra routines, e.g. eigenvalue computation.
- New non-matrix algebra items, e.g. quaternions, polynomes or
arbitrary precision floats.
- Symbolic manipulation. Could make something like
- New matrix optimization for new usage, e.g. integer matrices for
image processing or cryptography.
- Interface to new foreign libraries, e.g. GSL.
- More special functions.
2.6 Bugs and limitations
The purpose of Lisplab is to be a platform for
mathematical computations. From this perspective it
is clear that it will never be complete. Also, since there is no
spec it is not obvious what is a bug and what is not!
Hence, the list in this section must be read as non-systematic gathering
of problem features.
- Lisplab runs only on SBCL.
Lisplab is mainly ANSI Common Lisp, so just minor changes
in the build-system should make it
run on other lisps, but the problem is that it will most
probably be slow. It should be fast on CMUCL, though.
- Lacks a formal spec.
- Poorly tested.
- Lacks error checks (but these should not be made before a spec!)