Generate SIMD Code from MATLAB Functions for Intel Platforms

You can generate single instruction, multiple data (SIMD) code from certain MATLAB^® functions by using Intel^® SSE and, if you have Embedded Coder^®, Intel AVX technology. SIMD is a computing paradigm in which a single instruction processes multiple data. Many modern processors have SIMD instructions that, for example, perform several additions or multiplications at once. For computationally intensive operations among supported functions, SIMD intrinsics can significantly improve the performance of the generated code on Intel platforms.

To generate SIMD code by using the Embedded Coder Support Package for ARM^® Cortex^®-A Processors, see Generate SIMD Code from MATLAB Functions for ARM Platforms (Embedded Coder).

MATLAB Functions That Support SIMD Code for Intel

When certain conditions are met, you can generate SIMD code by using Intel SSE or Intel AVX technology. The following table lists MATLAB functions that support SIMD code generation. The table also details the conditions under which the support is available.

MATLAB Function	Conditions
`plus`	For AVX, SSE, and FMA, the input argument has a data type of `single` or `double`. For AVX2, SSE4.1, and SSE2, the input argument has a data type of `single`, `double`, `int8`, `int16`, `int32` or `int64`. For AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`. For integer data types, the value of Saturate on integer overflow is set to `No`.
`minus`	For AVX, SSE, and FMA, the input argument has a data type of `single` or `double`. For AVX2, SSE4.1, and SSE2, the input argument has a data type of `single`, `double`, `int8`, `int16`, `int32` or `int64`. For AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`. For integer data types, the value of Saturate on integer overflow is set to `No`.
`times`	For AVX, SSE, and FMA, the input argument has a data type of `single` or `double`. For AVX2, SSE4.1, and SSE2, the input argument has a data type of `single`, `double`, `int16`, or `int32`. For AVX512F, the input argument has a data type of `single` or `double`. For integer data types, the value of Saturate on integer overflow is set to `No`.
`rdivide`	The input argument has a data type of `single` or `double`.
`sqrt`	The input argument has a data type of `single` or `double`.
`ceil`	For AVX2, AVX, SSE4.1, SSE2, and SSE, the input argument has a data type of `single` or `double`. AVX512F is not supported.
`floor`	For AVX2, AVX, SSE4.1, SSE2, and SSE, the input argument has a data type of `single` or `double`. AVX512F is not supported.
`max`	For AVX, SSE, and FMA, the input argument has a data type of `single` or `double`. For AVX2, SSE4.1, and SSE2, the input argument has a data type of `single`, `double`, `int8`, `int16`, or `int32`. For AVX512F, the input argument has a data type of `single`, `double`, or `int32`. For integer data types, the value of Saturate on integer overflow is set to `No`.
`min`	For AVX, SSE, and FMA, the input argument has a data type of `single` or `double`. For AVX2, SSE4.1, and SSE2, the input argument has a data type of `single`, `double`, `int8`, `int16`, or `int32`. For AVX512F, the input argument has a data type of `single`, `double`, or `int32`. For integer data types, the value of Saturate on integer overflow is set to `No`.
`sum`	For AVX, SSE, and FMA, the input argument has a data type of `single` or `double`. For AVX2, SSE4.1, and SSE2, the input argument has a data type of `single`, `double`, `int8`, `int16`, `int32` or `int64`. For AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`. Optimize reductions is set to `on`. For integer data types, the value of Saturate on integer overflow is set to `No`.
`prod`	For AVX, SSE, and FMA, the input argument has a data type of `single` or `double`. For AVX2, SSE4.1, and SSE2, the input argument has a data type of `single`, `double`, `int16`, or `int32`. For AVX512F, the input argument has a data type of `single` or `double`. Optimize reductions is set to `on`. For integer data types, the value of Saturate on integer overflow is set to `No`.
`bitand`	For SSE2, the input argument has a data type of `int8`, `int16`, `int32`, or `int64`. For AVX2, and AVX512F, the input argument has a data type of `int8`, `int16`, `int32`, or `int64`.
`bitor`	For SSE2, the input argument has a data type of `int8`, `int16`, `int32`, or `int64`. For AVX2, and AVX512F, the input argument has a data type of `int8`, `int16`, `int32`, or `int64`.
`bitxor`	For SSE2, the input argument has a data type of `int8`, `int16`, `int32`, or `int64`. For AVX2, and AVX512F, the input argument has a data type of `int8`, `int16`, `int32`, or `int64`.
`bitshift`	The input argument has a data type of `int32` or `int64`.
`<`	For SSE4.1, the input argument has a data type of `single`, `double`, or `int32`. For AVX, the input argument has a data type of `single` or `double`. For AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`.
`<=`	For SSE4.1 and AVX, the input argument has a data type of `single` or `double`. For AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`.
`>`	For SSE4.1, the input argument has a data type of `single`, `double`, or `int32`. For AVX, the input argument has a data type of `single` or `double`. For AVX2, the input argument has a data type of `int32` or `int64`. For AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`.
`>=`	For SSE4.1 and AVX, the input argument has a data type of `single` or `double`. For AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`.
`==`	For SSE4.1, AVX, AVX2, and AVX512F, the input argument has a data type of `single`, `double`, `int32`, or `int64`.

If you have a DSP System Toolbox™, you can generate SIMD code from certain MATLAB System objects. For more information, see System objects in DSP System Toolbox that Support SIMD Code Generation (DSP System Toolbox).

Generate SIMD Code Versus Plain C Code

Consider the MATLAB function dynamic. This function consists of addition and multiplication operations between the variable-size arrays A and B. These arrays have a data type of single and an upper bound of 100 x 100.

function C = dynamic(A, B)
   assert(all(size(A) <= [100 100]));
   assert(all(size(B) <= [100 100]));
   assert(isa(A, 'single'));
   assert(isa(B, 'single'));

   C = zeros(size(A), 'like', A);
   for i = 1:numel(A)
       C(i) = (A(i) .* B(i)) + (A(i) .* B(i));
   end
end

To generate plain C code at the command line:

For C library code generation, create a code generation configuration object.
```
cfg = coder.config('lib');
```
To generate plain C code, set the InstructionSetExtensions property to None.
```
cfg.InstructionSetExtensions = 'None';
```
To generate a static library in the default location, codegen\lib\dynamic, use the codegen function.
```
codegen('-config', cfg, 'dynamic');
```

In the list of generated files, click dynamic.c. In the plain (non-SIMD) C code, each loop iteration produces one result.

void dynamic(const float A_data[], const int A_size[2], const float B_data[],
             const int B_size[2], float C_data[], int C_size[2])
{
  float C_data_tmp;
  int i;
  int loop_ub;
  (void)B_size;
  C_size[0] = (signed char)A_size[0];
  C_size[1] = (signed char)A_size[1];
  loop_ub = (signed char)A_size[0] * (signed char)A_size[1];
  if (0 <= loop_ub - 1) {
    memset(&C_data[0], 0, loop_ub * sizeof(float));
  }
  loop_ub = A_size[0] * A_size[1];
  for (i = 0; i < loop_ub; i++) {
    C_data_tmp = A_data[i] * B_data[i];
    C_data[i] = C_data_tmp + C_data_tmp;
  }
}

To generate SIMD C code at the command line:

For C library code generation, use the coder.config function to create a code generation configuration object.
```
cfg = coder.config('lib');
```
Set the coder.HardwareImplementation object TargetHWDeviceType property to 'Intel->x86-64 (Linux 64)' or 'Intel->x86-64 (Windows64)'.
```
cfg.HardwareImplementation.TargetHWDeviceType = 'Intel->x86-64 (Windows64)';
```
Set the coder.HardwareImplementation object ProdHWDeviceType property to 'Intel->x86-64 (Linux 64)' or 'Intel->x86-64 (Windows64)'
```
cfg.HardwareImplementation.ProdHWDeviceType = 'Intel->x86-64 (Windows64)';
```
If you are using the MATLAB Coder app to generate code:
- Set the Hardware Board parameter to None-Select device below.
- Set the Device vendor parameter to Intel, AMD, or Generic.
- Set the Device type to x86-64 (Linux 64), x86-64 (Windows64), or MATLAB Host Computer.
Set the InstructionSetExtensions property to an instruction set extension that your processor supports. This example uses SSE2 for Windows.
```
cfg.InstructionSetExtensions = 'SSE2';
```
The library that you choose depends on which instruction set extension your processor supports. If you use Embedded Coder, you can also select from the instruction sets SSE, SSE4.1, AVX, AVX2, FMA, and AVX512F.
For more information, see https://www.intel.com/content/www/us/en/support/articles/000005779/processors.html.
If you do not select an instruction set, the code generator uses the default setting Auto, which resolves to an instruction set according to your hardware:
- MATLAB Host Computer, Intel, or AMD^® － SSE2
- ARM
  － None
If you are using the MATLAB Coder app to generate code, on the Speed tab, set the Leverage target hardware instruction set extensions parameter to an instruction set that your processor supports.
Optionally, select the OptimizeReductions parameter to generate SIMD code for reduction operations such as sum and product functions.
```
cfg.OptimizeReductions = 'on';
```
If you are using the MATLAB Coder app to generate code, on the Speed tab, select the Optimize reductions parameter.
Use the codegen function to generate a static library in the default location, codegen\lib\dynamic.
```
codegen('-config', cfg, 'dynamic');
```

In the list of generated files, click dynamic.c.

void dynamic(const float A_data[], const int A_size[2], const float B_data[],
             const int B_size[2], float C_data[], int C_size[2])
{
  __m128 r;
  float C_data_tmp;
  int i;
  int loop_ub;
  int scalarLB;
  int vectorUB;
  (void)B_size;
  C_size[0] = (signed char)A_size[0];
  C_size[1] = (signed char)A_size[1];
  loop_ub = (signed char)A_size[0] * (signed char)A_size[1];
  if (0 <= loop_ub - 1) {
    memset(&C_data[0], 0, loop_ub * sizeof(float));
  }
  loop_ub = A_size[0] * A_size[1];
  scalarLB = (loop_ub / 4) << 2;
  vectorUB = scalarLB - 4;
  for (i = 0; i <= vectorUB; i += 4) {
    r = _mm_mul_ps(_mm_loadu_ps(&A_data[i]), _mm_loadu_ps(&B_data[i]));
    _mm_storeu_ps(&C_data[i], _mm_add_ps(r, r));
  }
  for (i = scalarLB; i < loop_ub; i++) {
    C_data_tmp = A_data[i] * B_data[i];
    C_data[i] = C_data_tmp + C_data_tmp;
  }
}

The SIMD instructions are the intrinsic functions that start with the identifier _mm. These functions process multiple data in a single iteration of the loop because the loop increments by four for single data types. For double data types, the loop increments by two. For MATLAB code that processes more data and is more computationally intensive, than the code in this example, the presence of SIMD instructions can significantly speed up the code execution time.

The second for loop is in the generated code because the for loop that contains SIMD code must be divisible by four for single data types. The second loop processes the remainder of the data.

For a list of a Intel intrinsic functions for supported MATLAB functions, see https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html.

Generate SIMD Instructions for MEX Code

To generate SIMD instructions in MEX code, set the MEX configuration parameter Hardware SIMD acceleration to one of these acceleration levels.

Full — AVX2
Portable — SSE2
None — No instruction sets

MATLAB Coder checks your specified hardware and compiler and uses the compatible intrinsics up to the level that you specify according to this list.

To generate SIMD instructions for MEX code at the command line, use the coder.MexCodeConfig object and set the 'SIMDAcceleration' property to 'Full', 'Portable', or 'None'.

Limitations

The generated code does not contain SIMD code when the MATLAB code meets these conditions:

Scalar operations outside a loop. For example, if a,b, and c are scalars, the generated code does not contain SIMD code for an operation such as c=a+b.
Indirectly indexed arrays or matrices. For example, if A,B,C, and D are vectors, the generated code does not contain SIMD code for an operation such as D(A)=C(A)+B(A).
Parallel for-Loops (parfor). The parfor loop does not contain SIMD code, but loops within the body of the parfor loop might contain SIMD code.
Polyspace^® does not support analysis of generated code that includes SIMD instructions. Disable SIMD code generation by setting the Leverage target hardware instruction set extensions parameter to None.