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SCILAB2C Requirements
Raffaele Nutricato
PoliBa
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Agenda
• Open Issues
• SCILAB2C specifications and limitations
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Open Issues: I/O
(Prof. Piazza) We need specifications
concerning the input and output data
management. Two approaches can be
adopted:
1. Input/Output files  SCI2C will support
file management functions
2. Input/Output buffers  SCI2C will not
support file management functions.
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hArtes Open Issues: Complex Numbers
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• Need to define an implementation for
complex numbers…
• For now we have a strong interface
(but it may decrease efficiency)
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************
We proposed 2 implementations: ************
• Standard C-99 :
#include <complex.h>
[…]
double a=1.0; double b=2.0;
float c=3.0; float d=4.0;
double complex z = a+b*I;
float complex w = c+d*I;
•
Hand made Complex structure with strong interface that hide the
real implementation :
(Only given as example, must be improve for efficiency)
#include <doubleComplex.h>
#include <floatComplex.h>
[…]
double a=1.0; double b=2.0;
float c=3.0; float d=4.0;
doubleComplex z = DoubleComplex(a,b);
floatComplex w = FloatComplex(c,d);
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hArtes Open Issues: Complex Numbers
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******* Implementation depends on compilation options *******
/*
** \function DoubleComplex
** \brief construct a Double Complex .
*/
doubleComplex DoubleComplex(double real, double imag) {
doubleComplex z;
#ifndef
STDC99
z.real = real;
/*
** Hand made Double Complex definition
** {
*/
struct double_complex
{
double real;
double imag;
};
typedef struct double_complex doubleComplex;
/*
** }
*/
z.imag = imag;
#else
z = real + I * imag;
#endif
return z;
}
/*
** Standard C99 Complex
** {
*/
#include <complex.h>
typedef double complex doubleComplex;
/*
** }
*/
But we may add another implementation
if it’s more efficient.
/*
** Another Hand made Double Complex definition
** {
*/
struct double_complex
{
double z[2];
};
typedef struct double_complex doubleComplex;
/*
** }
*/
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Open Issues: String
manipulation
• Do we need support for string
manipulation? Is it important for DSP?
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SCI2C specifications and
limitations
• Most of the specifications for the SCILAB2C tool
come from previous meetings with other hArtes
partners (mainly INRIA)
• They have been proposed in order to reach the
final goal (Dr. Gomez): to produce an
optimized code. “Optimized" means that
the generated C code does not include any
part of Scilab interpreter. So it should be
very small and efficient.
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SCI2C specifications and limitations:
Memory Optimization
Optimization of memory usage requires:
1.
Function Arguments are passed by reference.
2.
No dynamic memory allocation
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SCI2C specifications and limitations:
Memory Optimization
Function Arguments are passed by reference (in
Scilab/Matlab they are passed by value).
This means that:
•
the body of function cannot change the input parameter values.
or…
•
a warning can be issued by the SCILAB2C tool when a function
input prm is modified by the body of the function so that the user
can change the code according to the warning message.
Sol.:
Ex.:
Function c = test(a,b)
c = a + b;
a = rand();
c = c + a;
Warning here
Function c = test(a1,b)
a = a1;
c = a + b;
a = rand();
c = c + a;
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SCI2C specifications and limitations:
Memory Optimization
No dynamic memory allocation.
This means that:
•
No dynamic array extension
Ex.: A = [A B]; NOT ALLOWED!!!!
•
No support for Scilab t-list type.
•
The code must be annotated: users must specify
precision and size of data (for local/global vars and
function prms)
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SCI2C specifications and limitations:
Memory Optimization
Examples of code annotation:
Annotated version
//SCI2C: PRECISION float;
Non-Annotated
version
. . .
c = foo3(a,b);
function y = foo3(x,z)
y = abs(x) + det(z);
endfunction
Annotations avoids dynamic
memory annotation + crazy
code!!!
//SCI2C: a = complex2D(10,15)
//SCI2C: b = real2D(10,10)
//SCI2C: c = real2D(10,15)
. . .
c = foo3(a,b);
//SCI2C: y.size = x.size
//SCI2C: y.type = z.type
function y = foo3(x,z)
y = abs(x) + det(z);
endfunction
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Not known the size
of the memory that
have to be
allocated
SCI2C specifications and limitations: Memory
Optimization
Examples of crazy code:
Somtimes it can work, but if M =
random…
c = det(RandomMtx());
c = det(RandomMtx(M));
function B = RandomMtx()
M = round(10*rand());
B = rand(M,M);
endfunction
function B = RandomMtx(M)
B = rand(M,M);
endfunction
Rule: Non-crazy code is code that can be annotated by using relationships
as OutPrmSize=fnc(InPrmSize). This ensures that the sizes of
matrices can be computed at compile time.
A = rand(10,15)
C = transpose(A);
OK!!!
//SCI2C: B.size = [A.size(2), A.size(1)]
//SCI2C: B.type = A.type
function B = transpose(A)
B = A.’;
endfunction
A = rand(10,10)
C = determinant(A);
OK!!!
//SCI2C: B.size = [1, 1]
//SCI2C: B.type = real
function B = determinant(A)
B = abs(det(A));
endfunction
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SCI2C specifications and limitations: Memory
Optimization
Not known the size
of the memory that
have to be
allocated
Examples of crazy code:
Not known the size and type of the output
matrix
c = zeromatrix(2);
c = ReferenceMatrix(-11);
function B = zeromatrix(a)
if (a==1)
B = zeros(10,10);
end
if (a==2)
B = zeros(20,20);
end
if (a==3)
B = zeros(30,30);
end
endfunction
function B = ReferenceMatrix(a)
if (a==1)
B = zeros(10,10);
elseif (a==2)
B = zeros(20,20);
elseif (a==-1)
B = ones(10,10);
else
B = ones(20,20)+%i*ones(20,20);
end
endfunction
Non-crazy code is code that can be annotated by using relationships as
OutPrmSize=fnc(InPrmSize)
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SCI2C specifications and
limitations
• Functions with a variable number of input/output
arguments. In calling a function, users have always to
specify all input/output arguments. Prof. Piazza says
that it is a strong limitation. We can check (with
INRIA) if it is possible to guarantee variable
input/output arguments only for the SCI2C DSP library
(not for all the scilab functions written by the user).
• Change for the moment…
• SCI2C DSP library is the library that INRIA is
developing in C.
• The SCI2C DSP library will be composed of the
following functions:…
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SCI2C specifications and
limitations
Basic Statements
Support for:
•for
•while
•if, else, elseif
•Complex forms of the for statement.
From scilab help:
– Simple: for j = 2:3:n-1, a(j,j) = j; end;
– Complex: for e=eye(3,3),e,end
From Matlab help: Long loops are more memory efficient
when the colon expression appears in the FOR
statement since the index vector is never created.
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SCI2C specifications and
limitations
Auxiliary functions
• find
• isempty
• isnan
• rand
• Op:
• size
• type
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SCI2C specifications and
limitations
DSP functions
•
FIR
•
IIR
•
hilbert
•
conv
•
conv2d
•
cvexp
•
fft
•
ifft
•
levinson
•
lpc2cep
•
xcorrc
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SCI2C specifications and
limitations
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Elementary functions
•
acos
•
acosh
•
asin
•
asinh
•
atanh
•
cos
•
cosh
•
exp
•
log
•
log10
•
sin
•
sinh
•
sqrt
•
tan
•
tanh
•
vatan2
•
vexp10v
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SCI2C specifications and
limitations
Matrix Operators
eq
ne
lt
gt
le
ge
Relational operators.
- Equal
==
- Not equal
~=
- Less than
<
- Greater than
>
- Less than or equal
<=
- Greater than or equal
>=
and
or
not
Logical operators.
- Logical AND
&
- Logical OR
|
- Logical NOT
~
Arithmetic operators.
plus
minus
uminus
mtimes
times
mpower
power
mrdivide
rdivide
- Plus
- Minus
- Unary minus
- Matrix multiply
- Array multiply
- Matrix power
- Array power
- Slash or right matrix divide
- Right array divide
+
*
.*
^
.^
/
./
• abs, det, inv, mchol, sum, trace
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SCI2C specifications and
limitations
String handling ???
• part
• strcat
• strcmp
• strindex
• strsubst
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SCI2C specifications and
limitations
File Management ???
• mclose
• mget
• mopen
• mprintf
• mseek
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SCI2C specifications and
limitations
•
Max dimension is 2D
•
Each source file must contain only one function declaration  NO
multiple functions can be written into the same file.
•
N-ary expressions: Expressions as E=A+B+C+D will not be converted
as:
– E[i] = A[i] + B[i]+ C[i] + D[i].
– They will be converted as:
– E = sum(A,sum(B,sum(C,D)));
– To force the first implementation the user har to re-write the Scilab code as in
the following example:
– for i=1:N
–
E[i] = A[i] + B[i]+ C[i] + D[i]
– end
– No support for optimizations that are already available in the C compiler.
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SCI2C specifications and
limitations
• DATA TYPES SUPPORTED:
– TYPES: Real/Complex
– PRECISION: Float/Double
– Integer precision is supported only for the counter
variables of the “for” loops.
– The precision of data can be float or double and it is
the same for all the variables (except for counter
variables in the “for loops”).
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Control of temp variables
Support for temp variables generation control (remove
dependences + src != dest):
• a = (b + c + h) * (d + e);
• a = b + c;
There are some
• a = a + h;
mAgic functions
• temp1 = d + e;
that don’t allow
src=dest
• a = a * temp1;
Or
• temp1 = b + c;
• temp2 = temp1 + h;
• temp3 = d + e;
• a = temp2 * temp3;
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End Of Presentation
Thank you!
Raffaele Nutricato
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