the FISh language

	* Functional = Imperative + Shape *

FISh is a new array programming language that combines (and extends)

of functional programming with the 


of imperative, or procedural, programming by performing


on all programs. This shape computation reduces higher-order
functional programs to simple imperative forms, i.e. F - Sh = I.
Conversely, FISh works best when functions are constructed from
imperative procedures and shape functions, as recommended by the
slogan that gives the language its name.

	         F = I + Sh

FISh execution speeds on typical array problems are SEVERAL TIMES
FASTER than other higher-order, polymorphic languages.

This announcement, research papers, reference material and the
implementation are all available from


The main themes are introduced in the paragraphs below.

Barry Jay


Expressive Power 

FISh supports 

- the usual functional features

such as strong typing, higher-order functions and parametric
polymorphism. It also supports 

 - simple imperative features 

such as assignment, for- and while-loops, and local variables, using a
type of commands. Procedures are represented as functions into
commands, as in Algol-like languages. 

Unlike existing Algol-like languages, it also supports data types of
arrays, so that array programs may be polymorphic in the size of the
array. Now this has been extended to

 - poly-dimensional array programming.

If a is any array type then [a] is the type of all arrays with entries
in a that are

 - finite dimensional, and 
 - regular, 

such as vectors, matrices, three-dimensional arrays, etc. Thus,
poly-dimensional array programs can be written that act on a vector,
matrix, or higher-dimensional array. For example, the standard prelude
supports a polymorphic constant

              map : (a -> b) -> [a] -> [b] 

which will map a function over a vector, matrix, or higher-dimensional
array. Note that a matrix of integers, or type [int] has a different
type from a vector of vectors of type [[int]]. Thus, given 
sum_int : [int] -> int which adds up an array of integers, we have

             map sum_int : [[int]] -> [int]

which will sum each entry of the outer array. This is useful when 

 - representing complex numbers as arrays of length 2, or
 - each entry in an array has an associated array of data, e.g.
      - each entry is given an array of nearest neighbours, or
 - decomposing an array into an array of blocks, e.g. 
      - a matrix into a matrix of matrices.


|               |Map   Reduce  Q'sort  FFT   MM-loops  MM-ip |
|FISh           |1.04  0.37     1.93   3.57  4.36       7.05 |
|Ocaml(in-lined)|4.00  2.66     3.53                         |
|Ocaml          |5.99  4.59    15.47   8.16  7.71      60.61 |
|speedup        |5.8   12.4     8.0    2.3   1.8        8.6  |

		Benchmark user times 

See http://linus.socs.uts.edu.au/~cbj/FISh/Benchmarks for more

Static Shape Analysis

Shape analysis is used to determine the number of dimensions, and the
size in each dimension, of every array appearing in a program. (A
program is a closed term of array or command type.) In particular, it
checks that every array is regular, i.e. is a hyper-cube whose
entries all have the same shape. The latter requirement is necessary
to ensure that every entry in a vector of vectors has the same length,
i.e. that a vector of vectors is a matrix. As well as detecting
irregularities, it is able to detect all other shape errors, such as
multiplying matrices of innapropriate sizes, or having incompatible
numbers of dimensions. It is the power of shape analysis that makes
poly-dimensional programming feasible.

Knowledge of the shapes can be used to improve memory management
during compilation of the resulting imperative program. In particular,
FISh supports a clear stack discipline, and so does not require a
garbage collector. The existing FISh compiler translates programs in
to simple C code, which is then compiled and executed in the usual
way. This approach is also being applied in the design and
implementation of a parallel version of FISh, called GoldFISh.

F = I + Sh

The slogan is represented within the standard prelude by a function

     proc2fun : (var a -> var b -> comm) -> (#b -> #a) -> b -> a 

for converting procedures (and shapes) to functions. proc2fun pr sh x
uses the shape function sh and the shape #x of the array x to
determine the shape of the result. It then creates a local variable y
of this shape, and invokes the procedure pr on and a stored value of
x, finally returning the value of y.


Shape analysis is supported by a clean and powerful categorical
semantics, in which the shape-data (or shape-entry) decomposition is
represented by a pullback. For multi-dimensional arrays, this is

                      	       [a] ---------> List a
                                |   |           |
                                |___|           |
                          shape |               | map #
                                |               |
                               \/               \/ 
                           List N x #a -----> List #a 

where c takes the list of numbers ns and the a-shape sh and produces a
list of length given by the product of ns whose entries are all sh.
In other words, all entries of an array must have the same shape.


FISh supports the usual Hindley-Milner style of polymorphism. It also
supports polymorphism in array sizes (unlike earlier Algol-like
languages) and in the number of dimensions of an array. That is, FISh
supports polydimensional programming. For example, the map function
is defined to have type

                             map : (a -> b) -> [a] -> [b]

and so may act on a vector, matrix or higher-dimensional
array. However, the existence of the simple imperative features means
that this can be compiled into a sequence of nested for-loops
corresponding to the number of dimensions of the array argument. 
Polydimensionality is a form of *shape polymorphism*. 

Array Programming

Poly-dimensionality means that many array programs, e.g. numerical
recipes, can be written for any number of dimensions while maintaining
static type and shape checking. For example, FISh supports
poly-dimensional stencilling and a poly-dimensional difference
equation solver (see Sample Programs).

Parallel Programming 

Size and shape are important parameters in estimating the cost of
alternative parallel implementations. FISh has been designed to
support a parallel variant, called GoldFISh, currently under
development. GoldFISh will support additional parallel combinators for
some of the existing FISh functions that express the usual
second-order constants and also the usual array distributions (see
Sample Programs).