## Accelerate Code Using fiaccel

### Speeding Up Fixed-Point Execution with fiaccel

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.

### Running fiaccel

The basic command is:

fiaccelM_fcn

By default, `fiaccel`

performs the following actions:

Searches for the function

stored in the file*M_fcn*

.*M_fcn*`m`

as specified in Compile Path Search Order.Compiles

to MEX code.*M_fcn*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.

### Generated Files and Locations

`fiaccel`

generates files in the following locations:

Generates: | In: |
---|---|

Platform-specific MEX files | Current folder |

code generation reports (if errors or warnings occur during compilation) | Default output folder: fiaccel/mex/ |

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.

#### 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`

).

X (Overall Performance Rank) | Time | X/Best | X_m/X_mex |
---|---|---|---|

Trial 1: Best Performance | |||

`t_matrix_column_sum_m` (2) | 1.99759 | 84.4917 | 84.4917 |

`t_matrix_column_sum_mex` (1) | 0.0236424 | 1 | |

Trial 2: Worst
Performance | |||

`t_scalar_sum_m` (4) | 10.2067 | 431.71 | 2.08017 |

`t_scalar_sum_mex` (3) | 4.90664 | 207.536 |

### Data Type Override Using fiaccel

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

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

command in that
section:

fipref('DataTypeOverride','TrueDoubles')

This command tells Fixed-Point Designer 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 example,
the resulting MEX-function uses floating-point data.

### Specifying Default fimath Values for MEX Functions

MEX functions generated with `fiaccel`

use the MATLAB default global `fimath`

. The MATLAB factory default global `fimath`

has the following properties:

RoundingMethod: Nearest OverflowAction: Saturate ProductMode: FullPrecision SumMode: FullPrecision

When running MEX functions that depend on the MATLAB default `fimath`

value, do not change this value during your
MATLAB session. Otherwise, MATLAB generates a warning, alerting you to a mismatch between the compile-time and
run-time `fimath`

values. For example, create the following MATLAB function:

function y = test %#codegen y = fi(0);

`test`

constructs a `fi`

object without
explicitly specifying a `fimath`

object. Therefore, `test`

relies on the default `fimath`

object in effect at compile time. Generate the MEX function `test_mex`

to use the factory setting of the
MATLAB default
`fimath`

.

```
resetglobalfimath;
fiaccel test
```

`fiaccel`

generates a MEX function, `test_mex`

, in the current folder.Run
`test_mex`

.

test_mex

ans = 0 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 16 FractionLength: 15

Modify the MATLAB default `fimath`

value so it no longer matches the setting
used at compile time.

F = fimath('RoundingMethod','Floor'); globalfimath(F);

Clear the MEX function from memory and rerun it.

```
clear test_mex
test_mex
```

testglobalfimath_mex Warning: This function was generated with a different default fimath than the current default. ans = 0 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 16 FractionLength: 15

`fimath`

properties from your algorithm by
using types tables. For more information, see Separate Data Type Definitions from Algorithm.