Using fiaccel

Speeding Up Fixed-Point Execution with the fiaccel Function

You can convert fixed-point MATLAB code to MEX functions using fiaccel. The generated MEX functions contain optimizations to automatically accelerate fixed-point algorithms to compiled C/C++ code speed in MATLAB. The fiaccel function can greatly increase the execution speed of your algorithms.

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Running fiaccel

The basic command is:

fiaccel M_fcn

By default, fiaccel performs the following actions:

• Searches for the function M_fcn stored in the file M_fcn.m as specified in Compile Path Search Order.

• Compiles M_fcn to MEX code.

• If there are no errors or warnings, generates a platform-specific MEX file in the current folder, using the naming conventions described in File Naming Conventions.

• If there are errors, does not generate a MEX file, but produces an error report in a default output folder, as described in Generated Files and Locations.

• If there are warnings, but no errors, generates a platform-specific MEX file in the current folder, but does report the warnings.

You can modify this default behavior by specifying one or more compiler options with fiaccel, separated by spaces on the command line.

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Generated Files and Locations

fiaccel generates files in the following locations:

fiaccel/mex/M_fcn_name/html

You can change the name and location of generated files by using the options -o and -d when you run fiaccel.

In this example, you will use the fiaccel function to compile different parts of a simple algorithm. By comparing the run times of the two cases, you will see the benefits and best use of the fiaccel function.

Example: Comparing Run Times When Accelerating Different Algorithm Parts

The algorithm used throughout this example replicates the functionality of the MATLAB sum function, which sums the columns of a matrix. To see the algorithm, type open fi_matrix_column_sum.m at the MATLAB command line.

function B = fi_matrix_column_sum(A)
% Sum the columns of matrix A.
%#codegen
    [m,n] = size(A);
    w = get(A,'WordLength') + ceil(log2(m));
    f = get(A,'FractionLength');
    B = fi(zeros(1,n),true,w,f);
    for j = 1:n
        for i = 1:m
            B(j) = B(j) + A(i,j);
        end
    end

Trial 1: Best Performance

The best way to speed up the execution of the algorithm is to compile the entire algorithm using the fiaccel function. To evaluate the performance improvement provided by the fiaccel function when the entire algorithm is compiled, run the following code.

The first portion of code executes the algorithm using only MATLAB functions. The second portion of the code compiles the entire algorithm using the fiaccel function. The MATLAB tic and toc functions keep track of the run times for each method of execution.

% MATLAB
fipref('NumericTypeDisplay','short');
A = fi(randn(1000,10));
tic
B = fi_matrix_column_sum(A)
t_matrix_column_sum_m = toc

% fiaccel
fiaccel fi_matrix_column_sum -args {A} ...
-I [matlabroot '/toolbox/fixedpoint/fidemos']
tic
B = fi_matrix_column_sum_mex(A);
t_matrix_column_sum_mex = toc

Trial 2: Worst Performance

Compiling only the smallest unit of computation using the fiaccel function leads to much slower execution. In some cases, the overhead that results from calling the mex function inside a nested loop can cause even slower execution than using MATLAB functions alone. To evaluate the performance of the mex function when only the smallest unit of computation is compiled, run the following code.

The first portion of code executes the algorithm using only MATLAB functions. The second portion of the code compiles the smallest unit of computation with the fiaccel function, leaving the rest of the computations to MATLAB.

% MATLAB
tic
[m,n] = size(A);
w = get(A,'WordLength') + ceil(log2(m));
f = get(A,'FractionLength');
B = fi(zeros(1,n),true,w,f);
for j = 1:n
    for i = 1:m
        B(j) = fi_scalar_sum(B(j),A(i,j));
        % B(j) = B(j) + A(i,j);
    end
end
t_scalar_sum_m = toc

% fiaccel
fiaccel fi_scalar_sum -args {B(1),A(1,1)} ...
-I [matlabroot '/toolbox/fixedpoint/fidemos']
tic
[m,n] = size(A);
w = get(A,'WordLength') + ceil(log2(m));
f = get(A,'FractionLength');
B = fi(zeros(1,n),true,w,f);
for j = 1:n
    for i = 1:m
        B(j) = fi_scalar_sum_mex(B(j),A(i,j));
        % B(j) = B(j) + A(i,j);
    end
end
t_scalar_sum_mex = toc

Ratio of Times

A comparison of Trial 1 and Trial 2 appears in the following table. Your computer may record different times than the ones the table shows, but the ratios should be approximately the same. There is an extreme difference in ratios between the trial where the entire algorithm was compiled using fiaccel (t_matrix_column_sum_mex.m) and where only the scalar sum was compiled (t_scalar_sum_mex.m). Even the file with no fiaccel compilation (t_matrix_column_sum_m) did better than when only the smallest unit of computation was compiled using fiaccel (t_scalar_sum_mex).

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Using Data Type Override with fiaccel

Fixed-Point Toolbox software ships with a demonstration of how to generate a MEX function from MATLAB code. The code in the demo takes the weighted average of a signal to create a lowpass filter. To run the demo in the Help browser select Demos under Fixed-Point Toolbox, and then select the Fixed-Point Lowpass Filtering Using MATLAB for Code Generation demo.

You can specify data type override in this demo by typing an extra command at the MATLAB prompt in the "Define Fixed-Point Parameters" section of the demo. To turn data type override on, type the following command at the MATLAB prompt after running the reset(fipref) demo command in that section:

fipref('DataTypeOverride','TrueDoubles')

This command tells Fixed-Point Toolbox software to create all fi objects with type fi double. When you compile the code using the fiaccel command in the "Compile the M-File into a MEX File" section of the demo, the resulting MEX-function uses floating-point data.

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Setting Up a Supported C Compiler to Generate MEX Functions

Setting Up File Infrastructure and Paths

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