get started with gpu coder -凯发k8网页登录

gpu coder™ generates optimized cuda^® code from matlab^® code and simulink^® models. the generated code includes cuda kernels for parallelizable parts of your deep learning, embedded vision, and signal processing algorithms. for high performance, the generated code calls optimized nvidia^® cuda libraries, including tensorrt, cudnn, cufft, cusolver, and cublas. the code can be integrated into your project as source code, static libraries, or dynamic libraries, and it can be compiled for desktops, servers, and gpus embedded on nvidia jetson™, nvidia drive^®, and other platforms. you can use the generated cuda within matlab to accelerate deep learning networks and other computationally intensive portions of your algorithm. gpu coder lets you incorporate handwritten cuda code into your algorithms and into the generated code.

when used with embedded coder^®, gpu coder lets you verify the numerical behavior of the generated code via software-in-the-loop (sil) and processor-in-the-loop (pil) testing.

tutorials

matlab

• code generation by using the gpu coder app
  generate cuda code from matlab code by using the gpu coder app.
• 
  generate cuda code from matlab code by using the codegen command.
• verify correctness of the generated code
  behavioral verification of generated code, traceability, and code generation reports.
• 
  generate code for pretrained convolutional neural networks by using the cudnn library.
• 
  generate code for pretrained convolutional neural networks by using the tensorrt library.
• 
  suggestions for debugging cuda mex function.

simulink

• simulation acceleration by using gpu coder
  improve simulation speed by using nvidia gpus.
• 
  generate cuda code from simulink models by using gpu coder.
• 
  simulate and generate code for deep learning models in simulink using library blocks.

about code generation from matlab algorithms

• gpu programming paradigm
  

  introduction to gpu accelerated computing.

• gpu code generation workflow
  

  design, implement, and verify generated cuda mex for acceleration and standalone cuda code for deployment.

featured examples

gpu code generation: the mandelbrot set

generate cuda® code from a simple matlab® function by using gpu coder™. a mandelbrot set implementation by using standard matlab commands acts as the entry-point function. this example uses the codegen command to generate a mex function that runs on the gpu. you can run the mex function to check for run-time errors.

code generation for deep learning networks

get started with cuda code generation for image classification networks such as mobilenet-v2, resnet, and googlenet.