COMPILER TECHNIQUES FOR MATLAB PROGRAMS ... - CiteSeerX

Although not due to a limitation of our inference mechanism, the current version of
our compiler does not support global variables or sparse constructions, and supports
only a limited number of input/output commands. The support of global variables
and the reminder of the input/output constructions is straightforward, and should be
available in a second version of the compiler. Support for sparse computation requires
more research. This issue is being addressed for the FALCON system in the work by
Gallivan et. al. [GMBW95].
3.2 Phases of the MATLAB Compiler
The main challenge of the MATLAB compiler is to perform inference on the input program
to determine the variable properties: intrinsic type, rank, shape, and structure.
These
properties are used by the compiler to generate the Fortran 90 declarations and to optimize
the output code.
The MATLAB compiler was structured in a conventional way [ASU85] with a series of
dierent passes, as shown in Figure 3.1.
This section discusses the main issues and the
overall strategy adopted for each of the phases of the compiler.
3.2.1 Structure of a MATLAB Program
MATLAB is a procedural language. Its current version works with essentially one kind of
data structure: a rectangular numerical matrix [Mat92a]. A MATLAB program consists of
one or more Fortran-like statements which may include function calls.
There are two types of functions in MATLAB: intrinsic or built-in functions, and M-les.
Built-in functions range from elementary mathematical functions, such assqrt, log, and
sin, to more advanced matrix functions, such asinv (for matrix inverse), qr (for orthogonal
triangular decomposition), and eig (for eigenvalues and eigenvectors).
M-les consist of a sequence of MATLAB statements, which possibly include references
to other M-les. There are two types of M-les: scripts and functions. A script does not
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