MATLAB 코드에서 커널 생성

CUDA^® GPU 커널을 생성하는 MATLAB 코드 구조와 패턴

GPU Coder™는 MATLAB^® 코드의 특정 알고리즘 구조와 패턴에 최적화된 CUDA 커널을 생성하고 실행합니다. 생성 코드는 cuFFT, cuSolver, cuBLAS, cuDNN, TensorRT를 포함하여 최적화된 NVIDIA^® CUDA 라이브러리를 호출합니다. 생성 코드는 소스 코드, 정적 라이브러리 또는 동적 라이브러리로서 프로젝트에 통합할 수 있으며 데스크탑, 서버 및 NVIDIA Jetson, DRIVE를 비롯한 여러 플랫폼에 내장된 GPU에서 사용하도록 컴파일할 수 있습니다. GPU Coder를 사용하면 직접 작성한 CUDA 코드를 알고리즘과 생성 코드에 통합할 수 있습니다.

앱

모두 확장

GPU Coder

GPU Coder	MATLAB 코드에서 GPU 코드 생성
GPU 환경 검사	GPU 코드 생성 환경에 대한 확인과 설정

함수

모두 확장

코드 생성

`codegen`	Generate C/C++ code from MATLAB code
`gpucoder`	GPU Coder 앱 열기
`coder.checkGpuInstall`	GPU 코드 생성 환경 확인
`coder.gpuConfig`	Configuration parameters for CUDA code generation from MATLAB code by using GPU Coder

GPU 커널 프라그마

`coder.gpu.kernel`	Pragma that maps `for`-loops to GPU kernels
`coder.gpu.kernelfun`	함수를 GPU 커널에 매핑하는 프라그마
`coder.gpu.nokernel`	Pragma to disable kernel creation for loops
`coder.ceval`	Call C/C++ function from generated code
`coder.gpu.iterations`	Pragma that provides information to the code generator for making parallelization decisions on variable bound loops

GPU 메모리 프라그마

`coder.gpu.constantMemory`	Pragma that maps a variable to the constant memory on GPU
`coder.gpu.persistentMemory`	Pragma to allocate a variable as persistent memory on the GPU (R2020b 이후)
`cudaMemoryManager`	Query memory usage by shared GPU memory manager for MEX functions (R2024a 이후)

GPU 아토믹 연산

`gpucoder.atomicAdd`	Atomically add a specified value to a variable in global or shared memory (R2021b 이후)
`gpucoder.atomicAnd`	Atomically perform bit-wise AND between a specified value and a variable in global or shared memory (R2021b 이후)
`gpucoder.atomicCAS`	Atomically compare and swap the value of a variable in global or shared memory (R2021b 이후)
`gpucoder.atomicDec`	Atomically decrement a variable in global or shared memory within a specified upper bound (R2021b 이후)
`gpucoder.atomicExch`	Atomically exchange a variable in global or shared memory with the specified value (R2021b 이후)
`gpucoder.atomicInc`	Atomically increment a variable in global or shared memory within a specified upper bound (R2021b 이후)
`gpucoder.atomicMax`	Atomically find the maximum between a specified value and a variable in global or shared memory (R2021b 이후)
`gpucoder.atomicMin`	Atomically find the minimum between a specified value and a variable in global or shared memory (R2021b 이후)
`gpucoder.atomicOr`	Atomically perform bit-wise OR between a specified value and a variable in global or shared memory (R2021b 이후)
`gpucoder.atomicSub`	Atomically subtract a specified value from a variable in global or shared memory (R2021b 이후)
`gpucoder.atomicXor`	Atomically perform bit-wise XOR between a specified value and a variable in global or shared memory (R2021b 이후)

코드 생성을 위한 프로그래밍

`half`	Construct half-precision numeric object
`stencilfun`	Generate CUDA code for stencil functions (R2022b 이후)
`gpucoder.matrixMatrixKernel`	Optimized GPU implementation of functions containing matrix-matrix operations
`gpucoder.batchedMatrixMultiply`	Optimized GPU implementation of batched matrix multiply operation (R2020a 이후)
`gpucoder.stridedMatrixMultiply`	Optimized GPU implementation of strided and batched matrix multiply operation (R2020a 이후)
`gpucoder.batchedMatrixMultiplyAdd`	Optimized GPU implementation of batched matrix multiply with add operation (R2020a 이후)
`gpucoder.stridedMatrixMultiplyAdd`	Optimized GPU implementation of strided, batched matrix multiply with add operation (R2020a 이후)
`gpucoder.sort`	Optimized GPU implementation of the MATLAB sort function
`gpucoder.ctranspose`	Optimized GPU implementation of the MATLAB transpose function
`gpucoder.transpose`	Optimized GPU implementation of the MATLAB transpose function
`gpucoder.reduce`	Optimize GPU implementation for reduction operations

객체

모두 확장

코드 구성

`coder.gpuConfig`	Configuration parameters for CUDA code generation from MATLAB code by using GPU Coder
`coder.CodeConfig`	Configuration parameters for C/C++ code generation from MATLAB code
`coder.EmbeddedCodeConfig`	Configuration parameters for C/C++ code generation from MATLAB code with Embedded Coder
`coder.gpuEnvConfig`	Configuration object containing the parameters to check the GPU code generation environment

도움말 항목

Kernels from Element-Wise Loops
Create kernels from MATLAB functions containing scalarized, element-wise math operations.
Kernels from Scatter-Gather Type Operations
Create kernels from MATLAB functions containing reduction operations.
Kernels from Library Calls

Target GPU optimized math libraries such as cuBLAS, cuSOLVER, cuFFT, and Thrust.
- cuBLAS Example
- cuSOLVER Example
- FFT 예제
- Thrust 예제
Support for GPU Arrays
Generate CUDA code that uses GPU arrays.
Use Dynamically Allocated C++ Arrays in Generated Function Interfaces
Understand and use dynamically allocated arrays from the generated CUDA C++ function interfaces.
Call Custom CUDA Kernels from the Generated Code
Integrate custom CUDA kernels with MATLAB code intended for code generation.
Call Custom CUDA Device Function from the Generated Code
Integrate custom GPU device functions with MATLAB code intended for code generation.
Design Patterns
Create kernels for MATLAB functions containing computational design patterns.
GPU Memory Allocation and Minimization
Memory allocation options and optimizations for GPU Coder.
How Shared GPU Memory Manager Improves Performance of Generated MEX
GPU Coder creates a single universal memory manager that handles the memory management for all running CUDA MEX functions.
What Is Half Precision?
Introduction to the half-precision data type in MATLAB and Simulink^®.
Half Precision Code Generation Support
C/C++ and GPU code generation support for functions that support half-precision inputs.

추천 예제

Build a Map from Lidar Data Using SLAM on GPU

라이브 스크립트 열기

$Simulate Diffraction Patterns Using CUDA FFT Libraries$

Simulate Diffraction Patterns Using CUDA FFT Libraries

Use GPU Coder™ to leverage the CUDA® Fast Fourier Transform library (cuFFT) to compute two-dimensional FFT on a NVIDIA® GPU. The two-dimensional Fourier transform is used in optics to calculate far-field diffraction patterns. When a monochromatic light source passes through a small aperture, such as in Young's double-slit experiment, you can observe these diffraction patterns. This example also shows you how to use GPU pointers as inputs to an entry-point function when generating CUDA MEX, source code, static libraries, dynamic libraries, and executables. By using this functionality, the performance of the generated code is improved by minimizing the number of cudaMemcpy calls in the generated code.

스크립트 열기

QR Decomposition on NVIDIA GPU Using cuSOLVER Libraries

Create a standalone CUDA® executable that leverages the CUDA Solver library (cuSOLVER). The example uses a curve fitting application that mimics automatic lane tracking on a road to illustrate:

라이브 스크립트 열기

Stencil Processing on GPU

Generate CUDA® kernels for stencil type operations by implementing "Game of Life" by John H. Conway.

스크립트 열기

Benchmark Solving a Linear System by Using GPU Coder

Benchmark solving a linear system by generating CUDA® code. Use matrix left division, also known as mldivide or the backslash operator (\), to solve the system of linear equations A*x = b for x (that is, compute x = A\b).

라이브 스크립트 열기

Fog Rectification

The use of image processing functions for GPU code generation. The example takes a foggy image as input and produces a defogged image. This example is a typical implementation of fog rectification algorithm. The example uses conv2, im2gray, and imhist functions.

라이브 스크립트 열기

Stereo Disparity

Generate a CUDA® MEX function from a MATLAB® function that computes the stereo disparity of two images.

라이브 스크립트 열기