실행 속도

생성된 C/C++ 코드의 실행 속도 향상

코드 생성 옵션 및 최적화를 사용하여 생성 코드의 실행 속도를 향상시킵니다. 실행 속도에 영향을 줄 수 있는 동적 메모리 할당을 수정하거나 비활성화할 수 있습니다. parfor 루프를 사용하여 병렬화된 코드를 생성할 수 있습니다. 가능한 경우 기존의 최적화된 C 코드와 특화된 라이브러리를 활용하여 실행 속도를 높입니다.

특정 조건에 맞게 코드를 최적화하는 방법에 대한 자세한 내용은 Optimization Strategies 항목을 참조하십시오.

함수

`coder.areUnboundedVariableSizedArraysSupported`	Check if current configuration settings allow unbounded variable-size arrays (R2024a 이후)
`coder.ceval`	Call C/C++ function from generated code
`coder.const`	Fold expressions into constants in generated code
`coder.inline`	Control inlining of current function in generated code
`coder.inlineCall`	Inline called function in generated code (R2024a 이후)
`coder.loop.interchange`	Interchange loop indices in generated code (R2023a 이후)
`coder.loop.parallelize`	Parallelize specific `for` loops in generated code; disable automatic parallelization (R2021a 이후)
`coder.loop.reverse`	Reverse loop iteration order in generated code (R2023a 이후)
`coder.loop.tile`	Tile `for`-loops in the generated code (R2023a 이후)
`coder.loop.unrollAndJam`	Unroll and jam `for`-loops in the generated code (R2023a 이후)
`coder.loop.vectorize`	Vectorize `for` loops in generated code (R2023a 이후)
`coder.nonInlineCall`	Prevent inlining of called function in generated code (R2024a 이후)
`coder.unroll`	Unroll `for`-loop by making a copy of the loop body for each loop iteration
`coder.varsize`	Declare variable-size data
`parfor`	Parallel `for`-loop

클래스

`coder.BLASCallback`	Abstract class for specifying the BLAS library and CBLAS header and data type information for BLAS calls in generated code
`coder.LAPACKCallback`	Abstract class for specifying the LAPACK library and LAPACKE header file for LAPACK calls in generated code
`coder.fftw.StandaloneFFTW3Interface`	Abstract class for specifying an FFTW library for FFTW calls in generated code
`coder.loop.Control`	Loop optimization control object (R2023a 이후)

도움말 항목

생성 코드 최적화

Optimization Strategies
Optimize the execution speed or memory usage of generated code.
MATLAB Coder Optimizations in Generated Code
To improve the performance of generated code, the code generator uses optimizations.
Optimize Implicit Expansion in Generated Code
Implicit expansion in the generated code is enabled by default.

memcpy 및 memset 최적화

memcpy Optimization
The code generator optimizes generated code by using memcpy.
memset Optimization
The code generator optimizes generated code by using memset.

가변 크기 배열

Dynamic Memory Allocation and Performance
Dynamic memory allocation can slow down execution speeds.
Minimize Dynamic Memory Allocation
Improve execution time by minimizing dynamic memory allocation.
Provide Maximum Size for Variable-Size Arrays
Use techniques to help the code generator determine the upper bound for a variable-size array.
Disable Dynamic Memory Allocation During Code Generation
Disable dynamic memory allocation in the app or at the command line.
Set Dynamic Memory Allocation Threshold
Disable dynamic memory allocation for arrays less than a certain size.
Optimize Dynamic Array Access
Improve execution time of dynamic arrays in generated C code.

배열 레이아웃

Generate Code That Uses Row-Major Array Layout
Generate C/C++ code with row elements stored contiguously in memory.

루프

Algorithm Acceleration Using Parallel for-Loops (parfor)
Generate MEX functions for parfor-loops.
Classification of Variables in parfor-Loops
Variables inside parfor-loops are classified as loop, sliced, broadcast, reduction, or temporary.
병렬 for 루프(parfor)를 사용하여 코드 생성하기
공유 메모리 멀티코어 플랫폼에서 병렬로 실행되는 루프를 생성합니다.
Specify Maximum Number of Threads in parfor-Loops
Generate a MEX function that executes loop iterations in parallel on specific number of available cores.
Specify Maximum Number of Threads to Run Parallel for-Loops in the Generated Code
Run parallel for-loops on specific number of available cores in the generated code.
Reduction Assignments in parfor-Loops
A reduction variable accumulates a value that depends on all the loop iterations together.
Control Compilation of parfor-Loops
Treat parfor-loops as parfor-loops that run on a single thread.
macOS 플랫폼에 OpenMP 라이브러리 설치하기
macOS 플랫폼에서 병렬 for 루프를 생성하기 위해 OpenMP 라이브러리를 설치합니다.
Minimize Redundant Operations in Loops
Move operations outside of loop when possible.
Unroll for-Loops and parfor-Loops
Control loop unrolling.
Automatically Parallelize for Loops in Generated Code
Iterations of parallel for-loops can run simultaneously on multiple cores on the target hardware.
Reduction Operations Supported for Automatic Parallelization of for-loops
Supported operations for automatic parallelization of for-loops.
Generate SIMD Code from MATLAB Functions for Intel Platforms
Improve the execution speed of the generated code using Intel^® SSE and Intel AVX technology.
Optimize Loops in Generated Code
Generate code with loop transformations according to your performance requirements.

함수 호출

Avoid Data Copies of Function Inputs in Generated Code
Generate code that passes input arguments by reference.
Control Inlining to Fine-Tune Performance and Readability of Generated Code
Inlining eliminates the overhead of function calls but can produce larger C/C++ code and reduce code readability.
Fold Function Calls into Constants
Reduce execution time by replacing expression with constant in the generated code.

수치적 경계 조건

Disable Support for Integer Overflow or Nonfinites
Improve performance by suppressing generation of supporting code to handle integer overflow or nonfinites.

외부 코드 통합

LAPACK Calls in Generated Code
LAPACK function calls improve the execution speed of code generated for certain linear algebra functions.
BLAS Calls in Generated Code
BLAS function calls improve the execution speed of code generated for certain low-level vector and matrix operations.
Optimize Generated Code for Fast Fourier Transform Functions
Choose the correct fast Fourier transform implementation for your workflow and target hardware.
Integrate External/Custom Code
Improve performance by integrating your own optimized code.
Speed Up Linear Algebra in Generated Standalone Code by Using LAPACK Calls
Generate LAPACK calls for certain linear algebra functions. Specify LAPACK library to use.
Speed Up Matrix Operations in Generated Standalone Code by Using BLAS Calls
Generate BLAS calls for certain low-level matrix operations. Specify BLAS library to use.
Speed Up Fast Fourier Transforms in Generated Standalone Code by Using FFTW Library Calls
Generate FFTW library calls for fast Fourier transforms. Specify the FFTW library.
Synchronize Multithreaded Access to FFTW Planning in Generated Standalone Code
Implement FFT library callback class methods and provide supporting C code to prevent concurrent access to FFTW planning.

문제 해결

Troubleshooting parfor-Loops

Diagnose errors for code generation of parfor-loops.

Resolve Issue: coder.inline("never") and coder.nonInlineCall Do Not Prevent Function Inlining

Troubleshoot instances of coder.inline('never') not preventing inlining.

MEX Generated on macOS Platform Stays Loaded in Memory

Troubleshoot issues that occur when the source MATLAB^® code contains global or persistent variables that are reachable from the body of a parfor-loop.